rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Table 5: Excluding Force Changes With Corresponding Large Revenue Changes - Emerging</mark>**

|**FORCE**|**No. of**<br>**Episodes**<br>**With Data**<br>**Available**<br>A + C|**Good Signals as a**<br>**percentage of**<br>**possible good**<br>**signals**<br>A / (A + C)|**Bad Signals as a**<br>**percentage of**<br>**possible bad**<br>**signals**<br>B / (B + D)|**Noise/Signal**<br>**Ratio**<br>[B / (B + D)] /<br>[A / (A + C)]|**Conditional**<br>**Probability**<br>A / (A+B)|
|---|---|---|---|---|---|
|Compensation of Employees<br>Consumption of Fixed|231|16%|11%|**0.68**|23%|
|Capital|9|22%|20%|**0.92**|17%|
|Grants|114|11%|14%|**1.19**|15%|
|Subsidies|110|22%|13%|**0.62**|24%|
|Use of Goods and Services|142|8%|15%|**1.92**|10%|
|Social Benefits|234|9%|12%|**1.23**|14%|
|Other Expenses|127|17%|12%|**0.70**|22%|
|**TOTAL**|**242**|**16%**|**10%**|**0.63**|**25%**|

# **f. Generating Force Benchmarks**

So far in the analysis we have defined a signal as occurring when force is above the 92<sup>nd</sup> percentile. We now want to convert the percentile figure into a level of force to provide an indicative benchmark of when force would issue a signal. To do so we follow the methodology of the IMF (2013) who calculated benchmarks for different debt indicators in market-access countries. First, we take calculate the level of force at the 92<sup>nd</sup> percentile for each country. We then take the median of these values across all countries in order to provide an indicative benchmark value of force above which it signals a warning of future expenditure episodes.

|**Table 6: Indicative Benchmarks for 2-Y**<br>(percentage points)|**ear Force**|**Standard**|||
|---|---|---|---|---|
||**Share***|**Deviation****|**Advanced**|**Emerging**|
|**Compensation of Employees**|33.1|17|2.4|6.7|
|**Consumption of Fixed Capital**|4.5|0.8|0.3|1.7|
|**Grants**|9.9|15.8|1.5|10.1|
|**Subsidies**|5.9|8.9|2.8|10.2|
|**Use of Goods and Services**|21.0|14.2|1.3|7.3|
|**Social Benefits**|22.3|24.5|2.4|10.0|
|**Other Expenses**|5.9|8.7|2.7|6.3|
|**Total Expenses**|||11.0|20.5|

*Average of all country values (percent) ** Standard deviation of force across all countries (percentage points)

The benchmark level of force is affected by two sources. The first is the share of each component in total expenditure. Momentum is calculated by weighting the growth rates of, for example subsidies, by the share of subsidies in expenditure. For those components that are relatively small, such as consumption of fixed capital, will tend to have smaller benchmark values. The second component is the volatility of a
attrition is correlated with hours worked, this behavior would bias our results. We test for attrition bias following the procedure suggested by Verbeek and Nijman (1992), and described in Wooldridge (2002, Ch.17.7.2). This requires adding to the baseline regression an indicator equal to 1 if the household drops from the panel sample in the next period. Under the null hypothesis that attrition is not systematic, this additional explanatory variable should not be significant. We find that indeed this variable is insignificant and thus we fail to reject the null hypothesis. This result weakens concerns of attrition bias being a relevant issue (see Table B.4 in the Appendix).

# **4.3 Non-linearities**

The previous results provide evidence of the _average_ effect of PM2 _._ 5 on labor supply. However, these effects could be different at higher levels of pollution. This could happen, for instance, if health problems only become severe enough to require hospitalization or preclude work when pollution is sufficiently high. It could also be that different demographic groups react to different “critical” levels of air pollution.

To examine non linearities, we estimate the baseline model (1) replacing the main explanatory variable with a step function of PM2 _._ 5. In particular, we estimate the following model: where _PM_ 2 _._ 5<sup>_k_</sup> _j,t−_ 1<sup>isanindicatorequalto1ifaveragePM2</sup><sup>_._5inweek</sup><sup>_t −_1isinbracket</sup><sup>_k_.39</sup> Similarly to the baseline results, we split the sample between individuals in households with and without susceptible dependents.

Figures 4a and 4b display the estimates of _βk_ for both samples.<sup>40</sup> There are two important observations. First, for individuals with susceptible dependents (i.e., small children and elderly adults) the relationship between pollution and labor supply seems to be linear. The effect is negative and statistically significant even at moderate levels (i.e., around average). However, for individuals without susceptible dependents, the relationship is non-linear. At moderate levels

> 39We define the following brackets _k_ = _{_ 0 _−_ 35 _,_ 35 _−_ 45 _,_ 45 _−_ 55 _,_ 55 _−_ 75 _,_ 75+ _}_ . We define these brackets based on the breakpoints used in the U.S. Air Quality Index (U.S. EPA, 2012) and the constraint of having enough observations in each bracket.

> 40See Table B.5 in Appendix for the regression estimates.

20
- Gertler, Paul and Paul Glewwe. 1989. "The Willingness to Pay for Education in Developing Countries." Living Standards Measurement Study Working Paper No. 54, Washington, D.C.

- Gertler, Paul, Luis Locay and Warren Sanderson. 1987. "Are User Fees Regressive?: The Welfare Implications of Health Care Financing Proposals in Peru." Journal of Econometrics 33:67-88.

- Gertler, Paul and Jacques van der Gaag. 1990. The Willingness to Pay for Medical Care: Evidence from Two Developing Countries. Baltimore: Johns Hopkins University Press for The World Bank.

- Haddad, Lawrence and Ravi Kanbur. 1990. "How Serious is the Neglect of Intra-Household Inequality?" The Economic Journal 100:866-81.

- Haddad, Lawrence and Ravi Kanbur. 1993. "Is there an Intra-Household Kuznets Curve? Some Evidence for the Philippines." Public Finance 47(supplement):77-93.

- Jimenez, Emmanuel. 1987. Pricing Policy in the Social Sectors. Baltimore: Johns Hopkins University Press for The World Bank.

- Kesselman, J.R. 1982. "Taxation Behavior and the Design of a Credit Income Tax." In I. Garfinkel (ed) Income-Tested Programs: The Case for and Against. New York: Academic Press.

- King, Mervyn. 1983. "Welfare Analysis of Tax Reforms Using Household Level Data." Journal of Public Economics 21:183-214.

- Lal, Deepak. 1994. "Labor Market Insurance and Social Safety Nets." HROWP 41, Human Resources Development and Operations Policy Department, The World Bank, Washington, D.C.

- Lanjouw, Peter and Martin Ravallion. 1994. "Poverty and Household Size." Policy Research Working Paper 1332, The World Bank, Washington, D.C.

- Lipton, Michael and Martin Ravallion. 1994. "Poverty and Policy." In Behrman, Jere and T.N. Srinivasan (eds.) Handbook of Development Economics Volume 3. Amsterdam: North-Holland.

- Litvack, Jennie and Claude Bodart. 1993. "User Fees Plus Quality Equals Improved Access to Health Care: Results of a Field Experiment in Cameroon." Social Science and Medecine 37(3):36983.

- McKenzie, George W. 1983. Measuring Economic Welfare: New Methods. Cambridge: Cambridge University Press.

- Meerman, Jacob. 1979. Public Expenditure in Malaysia: Who Benefits and Why. New York: Oxford University Press for the World Bank.

- Nelson, Joan. 1992. "Poverty, Equity, and the Politics of Adjustment." In Haggard, Stephan and Robert Kaufman (eds.) The Politics of Economic Adjustment: International Constraints, Distributive Conflicts, and the State. Princeton., N.J.: Princeton University Press.

New York Times. 1993.

48
subject and curricular areas at the district level. For example, figure 2 displays (for a selected school district in Ecuador) the average performance of teachers in the district in curricular areas related to the teaching subject literature and language for UGB. A similar exercise could be replicated in other subject areas, such as mathematics for UGB, physics for UGB, and so forth. The figure plots the average percentage of correct answers in a given curricular area for all teachers in the district who currently teach this subject. The red, yellow, and green bars display the subjects on which teachers, on average, correctly answered less than 30 percent of the subject-related questions, between 30 and 35 percent of the subject-related questions, and between 40 to 60 percent of the subject-related questions, respectively. This information could be used by program managers and universities to develop a more pertinent training curriculum that responds to actual knowledge gaps of teachers in a given school district.

A similar exercise using the results of the _Ser Maestro_ assessments could be conducted at the school level. The MINEDUC could provide school directors with individualized teacher reports that contain similar information for all schoolteachers. Based on such information, teachers and directors could develop individualized training plans. Since at the district level the supply of services (in partnership with local universities and pedagogical institutes) would also consider the training needs of teachers in the district, teachers and directors could choose from the available courses offered at the district level the best ones that suit the teacher’s individual training needs. Of course, adapting the supply of services to the actual needs of teachers would take time. Also, for the process to work, the MINEDUC probably would need to provide technical assistance to school directors to be able to interpret teacher reports and to develop individualized training plans. The MINEDUC could start by testing this model in some school districts (and learn from the experience) before scaling up the model nationally.

13
However, as the lockdown continued in April, unemployment numbers rose by 9.8%. Yet unemployment in April 2020 was still 14.3% lower than it had been a year earlier.

**<u>Table 1. Main labor market indicators, February, March, and April 2019 and 2020</u>**

||<br>February|<br>March|<br>April|<br>% change|<br>(monthly)|
|---|---|---|---|---|---|
||[1]|[2]|[3]|[2] vs [1]|[3] vs [2]|
|||2019<br>||||
|[4] Employed<br>[5] Unemployed<br>[6] Inactive|3758.9<br>928.0<br>3261.8|3846.3<br>844.0<br>3254.4|3884.3<br>852.6<br>3203.9|2.3<br>-9.1<br>-0.2|1.0<br>1.0<br>-1.6|
|||2020||||
|[7] Employed<br>[8] Unemployed<br>|3779.2<br>771.6|3813.0<br>665.4|3839.3<br>730.3|0.9<br>-13.8|0.7<br>9.8|
|[9] Inactive|3353.1|3423.1<br>% change (ann|3329.4<br>ual)|2.1|-2.7|
|[7] vs [4]|0.5|-0.9|-1.2|||
|[8] vs [5]|-16.9|-21.2|-14.3|||
|[9] vs [6]|2.8|5.2|3.9|||

Source: Labor Force Survey (EL.STAT.) Notes: Seasonally unadjusted estimates for persons 15-74 years old.

In part, the falling unemployment rates reflect higher inactivity, as jobless workers were less likely to search for work. Table 1 confirms that more workers were inactive in February 2020 than one year earlier. The pandemic accentuated this trend: in March inactivity increased by 2.1%. However, labor force participation statistics need to be carefully interpreted while the lockdown is in place since some people who are counted as inactive might still have some attachment to the labor force. This is evident when we consider the “potential additional labor force” (PALF), which includes those “seeking work but who are not immediately available” and those “available for work and wanting to work but not currently seeking work” (Hornstein et al., 2014). According to LFS data, the size of the PALF increased by 40% in 2020Q1 compared to 2019Q1, and by 72% compared to 2019Q4.<sup>16</sup> This suggests that lockdown measures increased the underutilization of labor, with growing numbers awaiting recall, unable to look for jobs because of the lockdown, or discouraged by the lack of new job openings. As economic activity resumes, these marginally attached workers may be more likely to (re)join the labor force.

# **5.2 Unemployment claims**

Figure 4 shows the monthly number of recipients of unemployment insurance benefits. The top panel shows overall beneficiaries, demonstrating a pattern which reflects the seasonal character of the Greek economy. The bottom panel shows that new claims for unemployment insurance benefits increased slightly in February and March 2020, and then tripled in April 2020. However, in May, initial claims moved back to a level and trend similar to that of earlier years.

> 16 See ELSTAT Table 10 <u>here.</u>

19
**<u>Table 4. Jobs embodied in exports in 2011, in thousands</u>**

|Rk|C|Income||Jobsinex|ports<br>||Share of|
|---|---|---|---|---|---|---|---|
|an|ountry|group|Direct|Indirect|Ratio<br>direct/ indirect|Total jobs in $1<br>million of exports|JobX in<br>JobD|
|1|Australia|H-OECD|638.83|675.21|0.95|5.49|0.12|
|2|Austria|H-OECD|750.76|539.83|1.39|6.12|0.31|
|3|Belgium|H-OECD|824.26|579.37|1.42|3.42|0.31|
|4|Canada|H-OECD|1388.68|1452.60|0.96|6.37|0.16|
|5|Croatia|H-OECD|244.2|256.75|0.95|21.52|0.34|
|6|Czech Republic|H-OECD|877.92|867.80|1.01|11.14|0.36|
|7|Denmark|H-OECD|516.11|336.88|1.53|5.50|0.32|
|8|Estonia|H-OECD|141.45|86.78|1.63|13.09|0.37|
|9|Finland|H-OECD|296.19|256.04|1.16|5.99|0.22|
|10|France|H-OECD|2359.8|2105.64|1.12|6.32|0.17|
|11|Germany|H-OECD|5560.64|5340.57|1.04|6.90|0.27|
|12|Greece|H-OECD|477.14|270.48|1.76|11.52|0.18|
|13|Hungary|H-OECD|882.94|637.98|1.38|13.49|0.40|
|14|Ireland|H-OECD|447.62|451.30|0.99|3.83|0.49|
|15|Israel|H-OECD|340.92|266.49|1.28|7.34|0.20|
|...<br>52|Uruguay|UM|177.1|141.15|1.25|28.48|0.19|
|53|Venezuela, RB|UM|323.46|498.17|0.65|14.80|0.07|
|54|Armenia|LM|109.45|98.33|1.11|112.54|0.18|
|55|Ecuador|LM|581.51|679.81|0.86|64.19|0.20|
|56|Egypt, Arab Rep.|LM|1709.06|858.36|1.99|52.04|0.11|
|57|Guatemala|LM|939.09|615.17|1.53|118.65|0.29|
|58|Moldova|LM|150.92|136.69|1.10|103.49|0.25|
|59|Mongolia|LM|111.95|75.54|1.48|57.95|0.18|
|60|Morocco|LM|861.89|1140.37|0.76|66.84|0.19|
|61|Paraguay|LM|431.82|285.45|1.51|91.74|0.24|
|62|Philippines|LM|2758.88|4281.16|0.64|105.45|0.19|
|63|Thailand|LM|5105.1|10420.23|0.49|63.93|0.39|
|64|Ukraine|LM|2030.03|4128.07|0.49|77.22|0.30|
|65|Ethiopia|L|247.72|181.22|1.37|109.97|0.08|
|66|Zimbabwe|L|1489.62|434.30|3.43|735.12|0.35|

Note: Income group is defined according to the World Bank country group 2011 classification. H-OECD = high income OECD country; H = other high income country; UM = upper middle income country; LM = lower middle income country; L = low income country.

Figure 15 and Figure 16 plot the total number of jobs embodied in exports, split between direct and indirect jobs based on backward and forward linkages, respectively. The job content of exports is broken down by industry in 2011 (services, manufacturing, agriculture, and mining and energy extraction) aggregated across all countries in the database. It also presents the indicators at a more disaggregated level for eight services sectors. Our sample covers 66 countries in 2011, with total employment of 778.6 million, of which 30
Table A.19: EFM: Hindus vs Muslims

||Hindus<br>(1)|Muslims<br>(2)|
|---|---|---|
|**A. Death before 1 month**<br>_Firstborn girl_ * _Female_|1.581***<br>(0.428)|-0.254<br>(0.887)|
|_Firstborn girl_ * _Female_ * _Post_|-0.833*<br>(0.436)|0.709<br>(1.027)|
|N|241,234|47,775|
|**B. Death before age 5**|||
|_Firstborn girl_ * _Female_|2.860***<br>(0.676)|2.636**<br>(1.055)|
|_Firstborn girl_ * _Female_ * _Post_|-1.751**<br>(0.766)|-0.563<br>(1.209)|
|N|168,193|31,938|
|_Xijt_|x|x|
|_Firstborn Girl_ x Birth year FE|x|x|
|_Female_ x Birth year FE|x|x|
|_Female_ x State FE|x|x|
|_Female_ x Birth order FE|x|x|
|Birth order x Birth year FE|x|x|
|Birth order x State FE|x|x|
|State x Birth year FE|x|x|
|_Firstborn girl_ x State FE|x|x|
|_Firstborn girl_ x Birth order FE|x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression, with the same specification to column (3) in Table 3. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

66
Table 4.2. Per capita expenditure and the proportion of households with consumption drops by quintile

|||Pre-crisis|Post-crisis|Change|Prop.change|Hhsw/drops|
|---|---|---|---|---|---|---|
|_Allsample._|Mean|117.970|98.301|-19.670|-0.008|0.627|
||Std.err.|2.238|1.849|1.930|0.015||
||Median<br>Obs.|96.081<br>1766|80.027|-12.531|-0.151||
|_Quintile1._|Mean|39.684|55.244|15.559|0.487|0.304|
||Std.err.|0.597|1.533|1.509|0.046||
||Median|42.311|49.464|9.464|0.260||
||Obs.|354|||||
|_Quintile2._|Mean|68.896|71.659|2.763|0.051|0.582|
||Std. err.|0.397|1.955|1.982|0.030||
||Median|69.026|65.722|-4.810|-0.071||
||Obs.|353|||||
|_Quintile3:_|Mean|96.208|83.746|-12.462|-0.128|0.687|
||Std.err.|0.458|2.257|2.239|0.023||
||Median|95.834|78.775|-18.376|-0.195||
||Obs.|353|||||
|_Quntile4 _|Mean|132.797|112.583|-20.214|-0.144|0.722|
||Std.err.|0.724|2.887|2.962|0.023||
||Median|131.612|103.284|-29.809|-0.210||
||Obs.|352|||||
|_Quintile5 _|Mean|251.376|167.808|-83.569|-0.304|0.839|
||Std.err.|6.890|6.615|7.514|0.024||
||Median|213.353|142.771|-87.145|-0.409||
||Obs.|354|||||

Tables 4.3 to 4.5 look at the consumption level before and after the crisis and the change and the proportional change in consumption, by certain household characteristics. From table 4.3, one can see that large cities (Chisinau, Beltsy) had the highest level of consumption in Moldova both before and after the crisis. The post-crisis measure on average is even higher than the pre-crisis measure outside large cities. By the same token, they also experienced the highest drop in consumption level, on average. However, proportional to their initial consumption level, it is the non-rural small towns the ones more severely hit. It is interesting to note the large difference between the mean and the median in this table (as well as in the following ones), suggesting a very asymmetric impact on consumption even after stratifying by country region (or other characteristics).

In table 4.4, we look at the same variables by household size. This table shows that the smaller the household the larger the consumption drop and the proportional consumption drop. The table also shows that smaller households have higher per-capita consumption than larger households.

The information in table 4.5, where we control for household head's education level, is less clear. In general, it seems to be the case that households with more educated heads have higher levels of consumption. But there is a non-monotonic relationship between 10
Our application of this procedure to data for 1996, 2001 and 2007 led to results reported in both table 2.6 and figure 2.2. The decomposition for 2001 and 2007 is based on the same groups listed in table 2.5. As noted earlier, the data from the 1996 survey has a different grouping. These results confirm the conclusion we reached earlier on the basis of Shapley analysis of regional differences in Poverty. Between group inequality is indeed a major component of overall inequality (as measured by the Gini Coefficient) in Cameroon. This component has increased from 39 percent of the total in 1996 to almost 50 percent in 2007. It represented 43 percent of total inequality in 2001. These results also reveal that there is significant overlapping between regional distributions and a low level of within group inequality. In addition, within group inequality has been declining significantly over time. It accounted for about 30 percent of total inequality in 1996, 20 percent in 2001 and dropped to about 3 percent in 2007.

Table 2.7. Contribution of Location to Income Inequality

||Rural-Urban|Region|Rural-Urban &Region|
|---|---|---|---|
|1996|0.03|0.10|0.10|
|2001|0.18|0.17|0.22|
|2007|0.30|0.35|0.40|

Source: Authors’ Calculations Finally, we use simple regression analysis to decompose the variance of the logarithm of per adult equivalent expenditure. To do this, we run regressions the logarithm of per adult equivalent expenditure only on a set of dummy variables indicating the area of residence of the household. It is known that the R-squared from such a regression measures the proportion of the variation in the dependent variable (log of expenditure) explained by the location dummies (Benjamin, Brandt and Giles 2005). We consider three different specifications for each of the three years: the rural-urban divide alone, the regions only and the interaction between regional dummies and the rural-urban indicator. The results are presented in table 2.7. These results confirm that there is significant regional disparity in Cameroon and it has been growing over time. In 1996, rural-urban location accounted only for 3 percent of the variance of log per adult equivalent expenditure. In 2007, this proportion has increased to 30 percent. The regional dummies account for 10 percent of the variation in 1996 versus 35 percent in 16
# **Table 3(c): Probits for alternative measures of subjective poverty in Tanzania**

||(1)||(2)||
|---|---|---|---|---|
||Probit for p<br>oneself on<br>|lacing<br>step 1|Probit for placin<br>at or below vi<br>|g oneself<br>gnette 1|
||Coeff.|s.e.|Coeff.|s.e.|
|_Household Characteristics_|||||
|log per capita consumption|-0.431***|0.126|-0.556***|0.117|
|log household size|-0.038|0.244|-0.122|0.285|
|share of women|0.299|0.514|-0.261|0.454|
|share of children under age 6|-0.327|0.961|-1.180|1.026|
|share of children between 6<br>and 15|0.113|0.568|-0.154|0.621|
|share of members 55 and older|-0.914|0.637|-0.468|0.613|
|log land size (in acres)|-0.397***|0.101|-0.321***|0.093|
|_Household Head Characteristics_|||||
|household head female|1.087***|0.321|0.938***|0.288|
|household head age|0.013|0.011|0.012|0.010|
|household head years of<br>education|0.007|0.071|0.119*|0.069|
|_Respondent Characteristics_<br>respondent female|1.121***|0.252|0.981***|0.225|
|respondent age|0.007|0.012|-0.003|0.009|
|respondentyears of education|-0.061|0.077|-0.180**|0.078|
|Pseudo R<sup>2</sup>|0.185||0.186||

_Note:_ *** p<0.01, ** p<0.05, * p<0.1 23
<!-- Start of picture text --> ChartS. Daily exchange rate, LevI$1 Chart 6. Natural hedge for currency risk, 1998<br>Chart S. Daily exchange rate, Lev/$l<br>USD million<br>1.95 -800<br>I1.9 I<br>;185 __ 400 -<br>1.75 . . -_ I - tl<br>01.75 0 SDR -~ 1fM V,i .83) ( 0) ; 4R<br>t1.7 6 5.28) - ( (1-13 44<br>1.6 ,. 1.6 _ ---- -. , - , (431.73) (63.44) IC net exports 0 0 debt service I<br>Jan-98 Apr-98 Ju-98 Oct-98 Jan-99 Apr-99 Ju-99 Oct-99 (800)<br><!-- End of picture text --> Source: World Bank staff estimates.

The natural hedge on the dollar, however, is not complete and is on the wane. In 1998, the mismatch was some 20 percent of the dollar-denominated exports of US$70 million; in 1999 it is estimated to over 1/3. Chart 6 excludes dollar exposure on the dollar-denominated domestic debt, which constitutes another 10 percent. And there is a large quarter-on-quarter variance in the currency structure of debt service against foreign trade, exposing the country to short-term currency risk.

_Interest rate risk_ in public debt portfolio is a more serious reason for concern. The share of floating rate debt in both external and domestic debt portfolio was over three quarters at end1998. The drop in LIBOR over 1997-1998 made this to government's advantage, lowering its debt service costs. Possible increases in LIBOR in the future, however, will reverse this trend.

<!-- Start of picture text --> Chart 7. Stress testing: the impact of higher interest rates on the ratios of...<br>debt service-to-exports ... ... and debt service-to-budget revenues<br>30% ._.... 25%<br>25%, - 20% .<br>1 I- <~/<br>20%- 15%<br>1999 2000 2001 2002 2003 2004 H 1999 2000 2001 2002 2003 2004<br>j [ = ==:= 2=__^r_~~dW I i4 d=% ibaseline cq_-1I=1 _% idl=2°h C1 l3%;<br><!-- End of picture text --> Source: World Bank staff estimates.

16