rafmacalaba/datause-encoder-data huggingface dataset:
## **TABLE 9. ABSENTEEISM IN HONDURAS BY TYPE OF HEALTH WORKER AND** **<u>FACILITY</u>**

|**Absentee rate (%)**|Specialist|GP|Professional<br>nurse|Auxiliary<br>nurse|
|---|---|---|---|---|
|National hospital|33|52|26|25|
|Regional hospital|5|18|19|19|
|Urban health post|78|28|37|25|
|Peripheral clinic|80|87|47|61|

Source: Adapted from World Bank (2001).

Absenteeism is facilitated by weak performance incentives, lack of supervision, and an exceedingly low probability of being disciplined. While disciplinary action for health worker absences is often included in official regulations, in practice it is extremely rare.

# **Motivating health workers to raise their performance**

Many performance problems, including absenteeism, under-performance, and poor quality of service, stem from weak governance systems that fail to reward good performance and discipline workers who under-perform.

Health worker performance depends on a combination of skills, knowledge, and level of effort, the latter is largely driven by incentives and the degree of accountability in the system. Studies from India, Indonesia, Mexico, Paraguay, and Tanzania document variations in the quality of medical care using “medical vignettes and patient reports to benchmark the quality of medical advice against a checklist of essential procedures developed in consultation with doctors” (Das and Hammer 2005: 297). Overall, these studies concluded that the quality of health care was poor due to both inadequate knowledge and low levels of effort. The former can potentially be addressed through better training, the latter through better incentives, oversight, and accountability.

Table 10 lists non-financial incentives that appear to motivate health workers. The list is not exhaustive but illustrates the range of non-financial incentives that can influence health worker performance. These incentives are context-specific and should be viewed as possible tools to raise performance.

## **TABLE 10. NON-FINANCIAL INCENTIVES REPORTED BY HEALTH WORKERS** **<u>IN FOCUS GROUPS</u>**

|**Non-finan**|**cial incentives**|
|---|---|
|Career development prospects|Resource and equipment availability|
|Continuing education and training|Supervisor feedback|
|Professional recognition|Work environment|

Source: Authors.

Focus groups and interviews with health workers shed light on the importance of nonfinancial incentives in motivating health workers. Health workers in Benin emphasized the importance of “the ability to perform one’s work” by having the necessary resources combined with training and supervision (Mathauer and Inghoff 2006). A health worker 35
Table 15: Extensive margin II: Probability of export exit

|**Dependent Variable:**Probabilityof Ex|port Exit_f pjt_||||
|---|---|---|---|---|
|Big Firm: exports > median in|(1)|HS6 product-Year<br>(2)|HS2 sector-Year<br>(3)|Year<br>(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0545|-0.0671|-0.0596|-0.0010|
||(0.057)|(0.074)|(0.081)|(0.080)|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f_,_t_||0.0723|0.0272|-0.0659|
|||(0.102)|(0.105)|(0.100)|
|SPS_i_,_j_,_p_,_t_|0.1098|-0.0437|0.0408|-0.1746|
||(0.087)|(0.124)|(0.128)|(0.142)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f_,_t_||0.3218|0.1290|0.4476|
|||(0.168)*|(0.168)|(0.177)**|
|TBT_i_,_j_,_p_,_t_|0.1939|0.2654|0.3064|0.2809|
||(0.072)***|(0.106)**|(0.113)***|(0.116)**|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||-0.0746|-0.1811|-0.1256|
|||(0.150)|(0.149)|(0.143)|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0516|0.0292|0.0221|0.0468|
||(0.096)|(0.139)|(0.139)|(0.143)|
|Pre-Ship._i_,_j_,_p_,_t_x Big Firm_f_,_t_<br>||0.0108|0.0461|-0.0358|
|<br>3||(0.178)|(0.164)|(0.181)|
|Quantity Control_i_,_j_,_p_,_t_|-0.2712|-0.2904|-0.3151|-0.2507|
||(0.114)**|(0.133)**|(0.118)***|(0.120)**|
|Quantity Control_i_,_j_,_p_,_t_x Big Firm_f_,_t_||0.0857|0.0129|0.0921|
|||(0.095)|(0.090)|(0.088)|
|PriceControl_i_,_j_,_p_,_t_|-0.0495|-0.1460|-0.1276|-0.0254|
||(0.087)|(0.110)|(0.106)|(0.160)|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f_,_t_||0.1414|0.1469|-0.0319|
|||(0.118)|(0.117)|(0.155)|
|Log (Import Demand_f_,_p_,_j_,_t_)|-0.0211|-0.0208|-0.0217|-0.0215|
||(0.008)**|(0.009)**|(0.009)**|(0.009)**|
|Observations<br>|17221|16672|17077|17141|
|Adjusted_R_<sup>2</sup>|0.18|0.18|0.18|0.18|
|~~Fixed Effects~~<br>_~~f~~_~~−~~|_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~|_~~t~~_<br>_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|_~~f~~_~~− ~~_~~p~~_~~− ~~_~~j~~_~~,~~_~~f~~_~~− ~~_~~p~~_~~− ~~_~~t~~_~~,~~_~~j~~_~~− ~~_~~t~~_|

This table estimates equation 4 for the extensive margin II of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Policy ReseaRch WoRking PaPeR 4891

# **Abstract**

The initial occupational placements of male immigrants in the U.S. labor market vary significantly by country of origin even when education and other factors are taken into account. Does the heterogeneity persist over time? Using data from the 1980, 1990, and 2000 U.S. Censuses, this study finds that the performance of migrants from countries with lower initial occupational placement levels improves at a higher rate compared with that of migrants originating from countries with higher initial levels. Nevertheless, the magnitude of convergence suggests full catch-up is unlikely. Country specific attributes are found to have less direct impact on the rate of assimilation than on the initial performance.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to research determinants and implications of migration. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at ineagu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
WBES question order format. However, the difference is only statistically significant at the 10 percent level.

For the remaining 11 business environment elements, there is no statistically significant difference across the two groups. This may indicate that perceptions regarding these elements are stable, which is an important finding by itself. There is heterogeneity in these 11 business environment elements in terms of questionnaire structure. Some of them have extensive sections dedicated to them (access to finance, crime), while others hardly have a question (access to land). Some of them have sections that appear early in the questionnaire (transportation, customs and trade) while others appear late (access to finance, labor regulations). Thus, there is no obvious pattern that could explain why perceptions of the four business environment elements vary across the question order groups.

# _Regression Results Accounting for Additional Covariates_

Using equation (1), we now explore whether the differences of the 4 business environment elements disappear once several factors are accounted for in a regression analysis framework. The results are presented in table 2. The difference across the groups for electricity and political instability are no longer statistically significant as shown in table 2. The statistical significance remains for corruption and business, licensing and permits, at the 1 percent and 5 percent levels respectively – higher than the statistical significance of the corresponding mean differences. The difference across the groups is larger for corruption with the addition of controls (13.3 percentage points), but about the same for business, licensing and permits (7.7 percentage points).

There are some interesting results for both business environment elements regarding the controls. Firms with higher response rates in the crime and regulation sections are more likely to perceive corruption as a concern. While firms with higher response rates in the customs and trade section are more likely to perceive 14
2013 Report (World health Organization 2013) stated that the current tobacco smoking rate for male adults was 60.3% and 27.0% for female adults, 55.4% for male youth and 40.3% for female youth, based on Papua New Guinea STEPwise Surveillance (STEPS) for Noncommunicable Diseases conducted in 2007.

This paper uses the national representative sample to look into the current smoking patterns in Papua New Guinea. In summary, the results show that tobacco consumption imposes a significant financial burden to households, particularly to poor households. Tobacco consumption accounts for about 7% of total household expenditure and about 23% of total household food expenditure for the poorest quartile. Adults who cannot read are more likely to smoke. Household wealth status is significantly correlated with tobacco choices but not smoking prevalence. Smokers from the poorest quartile are more likely to consume non-processed tobacco, while smokers from the richest quartile are more likely to consume cigarettes.

# **2. DATA AND METHODS**

# **2.1 Data**

This paper uses the data from the 2009-2010 Papua New Guinea Household Income and Expenditure Survey (2009-2010 PNG HIES). The 2009-2010 HIES is the first comprehensive and nationally representative survey of the socioeconomic status of PNG households since the 1996 Household Survey of PNG conducted by Unisearch from the University of Papua New Guinea (UPNG) and the Institute of National Affairs (INA).

The 2009-2010 PNG HIES contains the final cross-section sample of 4,191 households. The sample of households was selected from a nationally representative sample frame. A comprehensive set of multi-topic questionnaires was designed to elicit information on key topics such as family demography, education, health, employment, and consumption. In addition to the standard questions on current smoking and ever smoking status, the survey also collects more indepth information on cigarette type and the number of cigarettes consumed daily for individuals who reported as currently smoking. The analyses on these data subsets can provide more evidence on smoking behaviors in Papua New Guinea to inform policy making.

The paper analyzes the overall smoking prevalence in Papua New Guinea. The smoking prevalence for youth in the 11-17 year old category is 5.5%. The male smoking rate in the category is 8% and the female smoking rate is 2.8%. The paper restricts the regression analysis to individuals 18 years old and above. The final sample contains 12,181 individuals (age 18 and over), with 6,239 males and 5,942 females, after excluding individuals with key variables missing (key variables are smoking status or choice of tobacco types).

# **2.2 Dependent Variables**

The two main dependent variables used are smoking status and the choices of tobacco among people who smoke. “Current smoking” is defined as currently smoking on a daily basis; “Ever smoked” is defined as smoking currently or previously. The choices of tobacco consumed include manufactured cigarettes, hand-rolled cigarettes, non-processed tobacco and cigars. Since only 6% 3
With respect to discriminatory restraints on foreign investors, Kenya requires that foreigners conduct business only in areas designated as general business areas. Local partners are encouraged, but not required. Expatriates employees are limited and the company must demonstrate that the skills are not available locally.

## **Professional Services**

There are rather severe restrictions on the rights of foreigners to operate with a license in many of the professional services sectors, including legal, accounting, auditing and engineering services. The East African Customs Union is making its first foray into commitments in the services areas by encouraging mutual recognition agreements among the members. In appendix D, we provide details on the situation in engineering services sectors.

# **III. Estimation of the Tariff Equivalence of the Regulatory Barriers**

Estimates of the ad valorem equivalents of the regulatory barriers in services are key to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 54 page survey of the regulatory regimes in key Kenyan business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services and maritime transportation services.<sup>15</sup> We supplemented this information based on a good set of studies on the services sectors that were presented at the conference on ―Trade in Services‖ in Nairobi, Kenya on March 26, 27, 2007. In particular, we examined the papers by: Kiptui (2007) on financial services; Ndaro (2007) or communication services; Helu (2007) on maritime services; Ochieng (2007) on transport services; and Oresi (2007) on railway services. The study by the World Bank (2007) provided additional detail on the key issues in the sectors and the Telecommunications Management Group (2007) provided extensive details on telecoms. Tarr (2007a, 2007b) summarized this information in telecommunications 15 We thank Ms. Sonal Sejpal of the law firm of Anjarwalla & Khanna Advocates for leading this research effort.

13
<!-- Start of picture text -->
-- i 2. “a qe . J<br>, : i> ° ener : j eS Po<br>er Oa OS<br>walues below the 25th percentile iv A<br>- MB values equal or grecter thon 25th and less than 50th percentile =.<br>~ Bl values equal or greater than 50th and less than 75th percentile ~<br>WB values cbove the 75th percentile<br><!-- End of picture text -->

|--<br>i<br>2.“<br>,<br>: i><br>°ene<br>er <br>waluesbelo<br>-<br>MBvaluesequal<br>~<br>Blvaluesequal<br>WBvaluescbov<br>|a<br>qe<br>r :<br> O<br>wthe25th <br>or grecter<br> or greater <br>ethe75th <br>|a<br>percentile<br>thon 25th <br> than 50th <br>percentile<br>|and lesst<br> and lesst<br>|jeS<br>OS<br>han 50th pe<br>han 75th pe<br>|.<br>J<br>iv <br>rcentile<br>rcentile ~<br>|Po<br> A<br>=.<br>||
|---|---|---|---|---|---|---|---|
|Assetprice volatility|countries ||Average|| Median|| deviation|||average|
|<br>World|84|33.6|33.1|10.8|11.3|67.3|38.0|
|Bydeveloped/developing economies||||||||
|Developed economies|41|34.1|33.3|10.0|11.7|67.3|32.8|
|Developing economies|43|33.2|31.7|11.6|11.3|58.8|39.3|
|Byincome level||||||||
|High income|41|34.1|33.3|10.0|11.7|67.3|32.8|
|Upper middle income|30|33.1|32.4|12.3|11.3|57.0|41.5|
|Lower middle income|11|34.8|32.0|10.6|21.0|58.8|37.3|
|Low income|2|26.9|26.9|0.1|26.9|27.0|27.1|
|Byregion||||||||
|High income: OECD|30|36.3|34.9|9.2|20.9|67.3|33.0|
|High income: non-OECD|11|28.2|30.6|10.0|11.7|45.3|30.5|
|EastAsia& Pacific|7|35.0|35.0|7.6|21.1|43.7|40.1|
|Europe& Central Asia|11|38.8|34.3|11.4|22.1|58.8|49.0|
|Latin America & Caribbean|10|34.0|34.3|14.9|11.3|57.0|42.3|
|Middle East & North Africa|5|21.9|21.0|6.7|13.4|32.0|27.1|
|South Asia|3|32.2|31.5|7.3|25.3|39.8|38.6|
|Sub-SaharanAfrica|7|30.1|27.0|10.5|16.4|48.1|28.0|

Source Global Financial Development Database, 2008-10 data.

Note: OECD = Organisation for Economic Co-operation and Development a. Weighted average by total population.
# **Annex 2: ICCs with interviewer covariates (Specification IV)**

||Hours per day<br>worked in primary<br>occupation by HH<br>head|Meals per day for<br>people over 5|Amount spent on<br>laundry detergent in<br>past 30 days|Mean difference<br>with respective<br>comparison group<br>(%)|
|---|---|---|---|---|
|**_Comparison 1 (n = 739, 746, 734)_**|**_.196_**|**_.229_**|**_.194_**||
||**_(.061)_**|**_(.068)_**|**_(.061)_**||
|Age (years)|.163|.22|.164|12.1|
||(.054)|(.066)|(.055)||
|**_Comparison 2 (n = 785, 792, 778)_**|**_.182_**|**_.219_**|**_.181_**||
||**_(.057)_**|**_(.064)_**|**_(.057)_**||
|Lives or has lived in Kassala (%)|.138|.188|.174|14.1|
||(.047)|(.06)|(.055)||
|Attended university or higher (%)|.172|.166|.162|13.4|
||(.055)|(.053)|(.053)||
|Conducted 10 or more CAPI|.172|.204|.145|10.7|
|surveys previously|(.055)|(.061)|(.049)||
|Self-rated skill with tablets or|.15|.211|.18|7.3|
|smartphones before survey (1 – 10)|(.05)|(.062)|(.057)||
|“On a scale of 1 to 10, how much|.182|.184|.177|6.1|
|do you generally trust other|(.057)|(.057)|(.056)||
|people?”|||||
|“On a scale of 1 to 10, how much|.182|.184|.177|6.1|
|do you think other people generally|(.057)|(.057)|(.056)||
|trust you?”|||||
|**_Comparison 3 (n = 738, 745, 731)_**|**_.178_**|**_.184_**|**_.152_**||
||**_(.058)_**|**_(.059)_**|**_(.052)_**||
|Work with CBS (%)|.163|.161|.124|13.1|
||(.054)|(.054)|(.045)||

14
||(9.370)|(9.242)|(9.397)|(9.319)|
|---|---|---|---|---|
|Observations|4,276|4,276|4,276|4,276|
|R-squared|0.130|0.133|0.133|0.133|
|Year x Country FE|Yes|Yes|Yes|Yes|

Note: *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses clustered at the bank level. All independent variables are lagged one year.

Figure 12 shows the estimates from the regression results reported in column 3, Table 2. The figure shows the impact on z-score of increasing capital ratios by 10% after all controls. The lines represent one standard error around the point estimates. The impact of simple leverage ratios on bank risk is both economically and statistically more significant than the impact of regulatory ratios on bank risk. A 10% increase in simple leverage would result in a bank moving from median to the 60<sup>th</sup> percentile in risk rankings after all controls and after holding regulatory capital constant.

**Figure 12: Relationship Between Bank Risk and Regulatory Capital and Simple Leverage**

<!-- Start of picture text --> Z-Score<br> 9.0<br> 8.0<br> 7.0<br> 6.0<br> 5.0<br> 4.0<br> 3.0<br> 2.0<br> 1.0<br> -<br>RC/RWA Equity/TA<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: We report the effect on the Z-Score of moving from increasing regulatory capital (RC/RWA) and simple leverage (Equity/TA) by 10%. The estimates are obtained after controlling for bank size (log(TA)), bank liquidity (LiquidA/TA), bank profitability (ROA), reliance on short-term funding (ShortFund/TA), and loan ratio (Loans/TA).

We also examine the impact on bank risk of having a higher proportion of bank capital in the form of Tier 1, which is captured by the coefficient on the variable Tier 1 Capital over Regulatory Capital (Tier 1C/ RC). In the second specification we capture the impact of having a higher portion of riskweighted assets, which is captured by the coefficient on the variable RWA/TA. We use the same bank control variables in both specifications reported in Table 1. All capital ratios and controls are lagged by one year. The coefficient on the Tier 1C/ RC variable captures the impact of having higher proportion of capital in the form of Tier 1. Since we control for the overall level of regulatory capital, the coefficient 15
from the first two surveys and three rounds from the last three surveys, with data from the BLSMS in 2001-2004,<sup>23</sup> the LECS in 2002/03-2007/08, the PSIDs in 2005, 2007, and 2009, the ENAHOs in 2004, 2005, and 2006, and the VHLSSs in 2004, 2006, and 2008. The number of households hovers around 2,376 households for Bosnia-Herzegovina, 6,500 households for the LECS, 9,189 households for each round of the VHLSSs, more than 5,000 households for the PSIDs,<sup>24</sup> and almost 20,000 households for the ENAHOs.

Except for the PSID that is implemented by the University of Michigan, all the other surveys are nationally representative surveys implemented by each country’s statistical agencies, with previous or current technical assistance from international organizations (the World Bank with Peru and Vietnam), leading universities (University of Essex with BosniaHerzegovina) or statistical agencies in richer countries (Statistics Sweden with Lao PDR). Also except for the PSID, all the other surveys are similar to the LSMS-type (Living Standards Measurement Survey) surveys supported by the World Bank in a number of developing countries and provide detailed information on household consumption and demographics, as well as schooling, health, employment, migration, and housing. The PSID has a more complex structure and provides similarly detailed, if not richer, information. All these surveys are widely used in academic studies (especially the PSID) as well as poverty assessment by the government and the donor community. We use the official poverty lines for Lao PDR, Peru, and Vietnam; for the USA, we use the poverty lines provided in the PSID data (which adjust for family size and demographics); for Bosnia-Herzegovina we use the 20<sup>th</sup> percentile of the consumption distribution in 2001 as the poverty line.

> 23 We build this dataset up on the basis of data from Demirguc-Kunt, Klapper and Panos (2011). 24 We only consider the sample persons in the PSID with non-zero longitudinal weights.

28
**Table 1 Correspondences between sectors of the model and two digit HS Classification**

|Agriculture, hunting, forestry and fishery|1-10|
|---|---|
|Mining and quarrying|25-27|
|Manufacturing|11-24, 28-97|
|Source: Authors' estimates.||

**Table 2 Armenian imports by sector and partner of the model, 2007 (in thousands of USD)**

||European<br>Union|CIS|Rest of the<br>World|
|---|---|---|---|
|Agriculture, hunting, forestry and fishery|15057|92032|127837|
|Mining and quarrying|145367|272520|98488|
|Manufacturing|753763|406486|1356241|

Source: Authors' calculations from data provided by the National Statistical Service of the Republic of Armenia.

**Table 3  Armenian exports by sector and partner of the model, 2007 (in thousands of USD)**

||European<br>Union|CIS|Rest of the<br>World|
|---|---|---|---|
|Agriculture, hunting, forestry and fishery|3192|5068|16640|
|Mining and quarrying|72795|2340|98404|
|Manufacturing|317146|208070|428649|

Source: Authors' calculations from data provided by the National Statistical Service of the Republic of Armenia.

**Table 4  Armenian collected tariff rates by sector and partner of the model, 2008 (in %)**

||Weighted<br>average|European<br>Union|CIS|Rest of the<br>World|
|---|---|---|---|---|
|Agriculture, hunting, forestry and fishery|5.2|8.5|0|8.5|
|Mining and quarrying|0|0|0|0|
|Manufacturing|3.2|3.8|0|3.8|

Source: Authors' calculations from data provided by the Armenian Customs Authority.

73