rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Associations between Program-Level Contribution to Graduation, Quality Determinants, and Local Market Conditions**

||Full S|ample|Large<br>Cities|Small/Med<br>Cities|Two-year<br>Programs|Three-year<br>Programs|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Program Characteristics_|||||||
|Three-year program|3.33|3.48|4.34<sup>*</sup>|-0.08|||
||(2.54)|(2.47)|(2.47)|(4.17)|||
|Distance program|-2.78|-3.53|-10.43<sup>**</sup>|0.50|-3.96|-3.67|
||(3.73)|(3.65)|(4.23)|(3.81)|(6.17)|(4.28)|
|Program age (years)|-0.88<sup>***</sup>|-0.77<sup>***</sup>|-0.48<sup>*</sup>|-1.18<sup>***</sup>|-0.45|-0.72<sup>**</sup>|
||(0.27)|(0.28)|(0.28)|(0.43)|(0.62)|(0.31)|
|_Institution Characteristics_|||||||
|Public HEI|0.72|0.72|0.80|-3.27|-7.96|3.71|
||(3.14)|(3.11)|(4.24)|(4.36)|(4.89)|(3.48)|
|Technological school|3.13|2.37|4.95|-0.95|0.10|1.71|
||(2.92)|(2.83)|(3.08)|(4.95)|(5.07)|(3.25)|
|University|-3.05|-3.43|-2.63|0.42|4.61|-5.68<sup>*</sup>|
||(3.19)|(3.04)|(3.56)|(4.58)|(7.19)|(3.25)|
|Institution size (log enrollment)|-2.69<sup>**</sup>|-2.08<sup>**</sup>|-3.06<sup>**</sup>|0.52|0.38|-3.15<sup>***</sup>|
||(1.03)|(1.00)|(1.31)|(1.40)|(1.96)|(1.17)|
|_Program and Institution Practices_|||||||
|Selective institution|0.08|0.93|1.20|1.72|6.78|-1.42|
||(2.95)|(2.92)|(3.72)|(4.56)|(5.44)|(3.13)|
|Institution field specialization (index)|-6.43|-3.52|4.16|-12.87<sup>**</sup>|5.20|-5.04|
||(3.96)|(4.22)|(4.73)|(6.17)|(6.81)|(5.19)|
|High-quality accreditation|1.63|1.58|-0.47|-3.13|3.18|2.30|
||(2.79)|(2.63)|(2.27)|(9.06)|(7.30)|(2.52)|
|_Local Market Conditions_|||||||
|Institution market power (index)|13.25<sup>*</sup>|9.74|4.44|9.60|1.62|11.53|
||(7.34)|(8.33)|(12.36)|(8.81)|(12.54)|(10.80)|
|Market concentration (HHI index)|-8.07|-12.49<sup>*</sup>|48.73<sup>**</sup>|-9.02|-5.99|-14.37<sup>*</sup>|
||(7.10)|(7.03)|(18.76)|(7.92)|(11.79)|(8.30)|
|City size (log population)|0.42|-1.00|-4.61|-1.13|-3.67|-0.04|
||(1.31)|(1.32)|(3.71)|(2.33)|(3.94)|(1.36)|
|Constant|17.40|32.91<sup>*</sup>|87.32|24.61|48.79|31.70|
||(19.67)|(19.38)|(52.95)|(33.34)|(58.90)|(19.15)|
|Field FE||X|X|X|X|X|
|Mean of Dependent Variable|0.00|0.00|0.28|-0.51|-2.17|0.98|
|Adjusted R-squared|0.08|0.12|0.25|0.10|0.12|0.13|
|Observations|621|621|356|265|192|429|

_Source_ : Authors’ calculations using administrative data. See Appendix 1 for the definition of the variables. _Notes_ : This table reports the associations between program-level contribution to graduation (between 0 and 100), quality determinants, and local market conditions. See Figure A4, Panel A for an illustration of coefficients’ relative size. A program is the unit of observation. All variables are dummies except when the unit of measure is indicated in parentheses. Indices take a value between 0 and 1. Regressions are weighted by 1/v, where v is the standard deviation of the estimated programlevel contribution. Specifications control for field fixed effects (FE), except in Column (1). Standard errors clustered at the institution level are in parentheses. Significance levels: * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. HHI=Herfindahl-Hirschman Index.

43
|**Legal status(ref cat = so**<br> <br>|**leproprietors**|**hip): **|||||
|---|---|---|---|---|---|---|
|**Publicly**<br>**listed**<br>**company**|0.000498|0.000778|0.000691|0.000687|0.000400|0.000483|
||(0.00195)|(0.00195)|(0.00198)|(0.00193)|(0.00207)|(0.00194)|
|**Limited**<br>**Liability**<br>**Company**|0.00106|0.00118|0.00101|0.00112|0.000851|0.00105|
||(0.00124)|(0.00126)|(0.00125)|(0.00123)|(0.00124)|(0.00127)|
|**Partnership**|‐0.00506|‐0.00470|‐0.00505|‐0.00510|‐0.00511|‐0.00477|
||(0.00307)|(0.00328)|(0.00335)|(0.00312)|(0.00317)|(0.00314)|
|**Limited Partnership**|‐0.000435|‐0.000239|‐0.000428|‐0.000358|‐0.000798|‐0.000362|
||(0.00118)|(0.00120)|(0.00117)|(0.00116)|(0.00120)|(0.00118)|
|**Other**|‐0.00100|‐0.000924|‐0.00179|‐0.00106|‐0.00183|‐0.000994|
||(0.00307)|(0.00314)|(0.00323)|(0.00312)|(0.00289)|(0.00313)|
|**Constant**|1.802***|1.958***|1.942***|1.814***|1.869***|1.878***|
||(0.238)|(0.259)|(0.249)|(0.244)|(0.239)|(0.246)|
|**Fixed effects**|Two‐digit ind|ustrydummies,|and countryand|region(within cou|ntry)fixed effects||
|**Observations**|1,089|1,089|1,089|1,089|1,089|1,089|
|**R‐squared**|0.801|0.801|0.802|0.800|0.804|0.801|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions.

It is possible that some of the individual management practices are likely to result in higher levels of productivity in larger establishments only. For example, there may be diminishing returns to the number of performance targets monitored in smaller enterprises; similarly, in smaller enterprises the question regarding the basis of performance bonuses may be less relevant. As such, the relationship between management and productivity is estimated separately for larger establishments (those with 50 or more employees). The results are displayed in Table 8 (which presents an abridged version of the results; the full results tables can be found in Appendix 2); Panel A of Table 8 shows that the relationship between the average management score and labor productivity is stronger for larger establishments (the coefficient on the average management score increases from 0.537 for all firms to 0.735 for large firms, although the difference is not statistically significant at 95 percent confidence level). Looking at the effect of the individual management practices on labor productivity, columns (2) through (6) of Table 5 show no significant effects, although the lack of statistical significance is likely to be due, in part, to a reduced sample size.

Turning to the total factor productivity results for establishments with 50 or more employees, Panel B of Table 8 shows that the effect of the average management score on total factor productivity results is of much greater magnitude for these establishments—the coefficient increases from 0.21 to 0.87 and becomes statistically significant. Furthermore, for this subset of larger establishments, there is a link between total factor productivity and the component of management that measures establishments’ response to problems in production, and the component that represents the basis of managers’ 19
However, the cohorts who obtained early childhood schooling before 1981 do not seem to have benefited as much. The reform facilitated the absorption of the large secondary school expansion, despite the decline in public spending for education. However, because of the secondary school expansion, lower ability students (from “middle class” families or otherwise) may have been admitted into the system, students who had earlier received their basic education in public schools. This group entered the labor market less endowed in schooling cognitive skills. As a result, the labor marker rewarded these skills significantly; failure to control for such skills in the earnings function results in a return to schooling estimate which is seriously biased upwards. The non-cognitive component of the return to schooling for this group is rather small (about half of that of the younger cohort).

The IV regressions using a treated group which consists of a mixture of basic and secondary age students in 1981, result in estimates of gross returns (column 1) as well as schooling returns with the cognitive skills component netted out (column 4) which are smaller that the corresponding OLS estimates, at about 8 and 6 percent respectively. On the other hand, comparing the results from using a treated group consisting of only basic school age students to those using a mixture of basic and secondary age students in 1981, reveals considerable heterogeneity in the returns to schooling in Chile.

# **Conclusions**

We use the sweeping school reform of 1981 in Chile to identify a binary instrument and estimate returns to schooling from IV, with and without controlling for cognitive ability. Given that ability bias needs to be dealt with, accounting for cognitive skills avoids the problem of using a possibly invalid instrument.

28
# **<u>ANNEX 1. DATA SOURCES AND METHODS</u>**

## **General Issues**

1. To ensure consistency across the World Bank and other data sources, the paper uses the OECD DAC definition of “developing country.” As such, certain IBRD countries are not reflected in the data (for example, on guarantees). According to the DAC, the term “developing country” employed without qualification has generally been taken to mean a country eligible for ODA. Other organizations have their own definitions. The World Bank usually uses the term to refer to low- and middle-income countries, assessed by reference to per capita GNI. The new DAC List of ODA Recipients is consistent with World Bank practice, except that it excludes countries that are members of the G8, or the EU, or that have a date of admission to the EU. This means that as of 2005, it excludes the following middle-income countries: _Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Russia, Slovak Republic, and  Slovenia._

2. All data are in current prices, US$.

3. While the bulk of the data was presented in calendar years, World Bank Group data on financial solutions (for example, those involving guarantees) were presented in fiscal years. Over the period reviewed, differences between calendar and fiscal years were found to be marginal.

4. Given the diversity of instruments presented in this paper (for example, cash and derivative instruments), data on innovative applications or uses does not differentiate between disbursements and commitments.

## **Specific Issues**

5. <u>Philanthropy and Private Donors: All data are from OECD DAC Table 1, grants by</u> private voluntary agencies. These were verified with data from McKinsey’s report to Bank.

6. <u>Guarantees and Insurance: All data on guarantees were derived from financial statements</u> of export credit agencies and MDBs, except for the World Bank Group where information was gathered internally. No effort was made to harmonize financial and calendar years. Data on export credit agencies might include a few nondeveloping countries as amounts were provided by region. Data expressed in currencies different from US$ were converted into US$ by using end of the year exchange rates. Data used in the analysis reflects guarantee amounts.

7. <u>Private Participation in Infrastructure (PPI): All data on PPI were derived from the online</u> PPIAF’s PPI database. Guarantees were excluded to avoid double counting. Data refer only to the share of PPI that was supported by donors.

8. <u>Carbon Trade: The value of CERs paid in each year was calculated using the UNFCCC</u> database for the number of CERs issued during each year. The amount was then multiplied by the prevailing primary market price two years before the CERs were issued. This time lag is an approximation to account for the fact that CERs are mostly sold through ERPAs, which lock in the price.

9. <u>IFFIm: Data from IFFIm financial statements were also verified by World Bank Trust</u> Fund data on encashments. Disbursement data were received from relevant implementing agencies.

38
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**Table 5: Summary of First stage regression results of 2SLS Estimation of Expors Equation**

||All Industr<br> <br>|y<br> <br> <br>|Garments and Te<br> <br>|xtiles<br>|
|---|---|---|---|---|
|Endogenous regressor|Shea Partial<br>|Partial<br>|Shea Partial<br>|Partial|
||R-squared<br>|R-squared<br>|R-squared<br>|R-squared|
|National supplier access|0.36|0.97|0.28|0.97|
|Local supplier access|0.25|0.71|0.15|0.54|
|Foreing market access|0.09|0.45|0.11|0.39|
|Log (employment)|0.03|0.19|0.01|0.01|
|Log (age)|0.01|0.06|0|0.01|
|Foreingowned|0.04|0.08|0.01|0.04|

31
Table 4: ITT Treatment Effect Estimates, Long-Run Business Outcomes Difference-in-Difference OLS

|Panel A: ITT Regression results|||Outcome|Variable|||
|---|---|---|---|---|---|---|
||Num<br>|ber of emplo<br>|yees<br>|Dail<br>|ywage bill(U<br>|SD)<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment*Post|||4.429**|||98.74*|
||||(1.686)|||(43.594)|
|Treatment (=1 if mean is for treatment gro|up)||-1.766**|||-30.602**|
||||(0.540)|||(10.379)|
|Post (=1 for years 2010, 2011, and 2012)|||2.148***|||35.191**|
||||(0.541)|||(10.182)|
|Constant|||7.991***|||136.76***|
||||(0.537)|||(7.251)|
|Number of observations|||10|||10|
|Panel B: Raw data|Treatment<br>Mean<br>(Std Dev)|Control<br>Mean<br>(Std Dev)|Difference<br>(P-value)|Treatment<br>Mean<br>(Std Dev)|Control<br>Mean<br>(Std Dev)|Difference<br>(P-value)|
|2005|6.169|7.402|-1.234|98.02|128.81|-30.79|
||(13.226)|(16.490)|(0.544)|(154.126)|(248.604)|(0.289)|
|2006|6.281|8.579|-2.298|114.29|144.70|-30.41|
||(11.865)|(18.853)|(0.298)|(159.164)|(293.049)|(0.365)|
|2010|9.787|10.262|-0.476|162.98|161.89|1.09|
||(35.958)|(21.181)|(0.895)|(353.039)|(311.735)|(0.980)|
|2011|14.067|10.098|3.970|263.02|169.10|93.92|
||(66.707)|(19.916)|(0.480)|(717.155)|(321.992)|(0.153)|
|2012|14.551|10.055|4.496|294.252|184.856|109.40|
||(67.984)|(20.668)|(0.434)|(798.781)|(343.924)|(0.131)|
|Number of enterprises|89|164|253|89|164|253|

Note: Administrative data from Mexico's Social Security Institute (IMSS) for years 2005, 2006, 2010, 2011, and 2012. All enterprises are required by law to register their workers with IMSS (although compliance is not universal). 57% of the enterprises in our sample were found in IMSS records. Both number of employees and daily wage bill refer to permanent employees with pay. Column 3 in Panel A displays the results from a regression of mean number of employees on a dummy for the mean being for the treatment group, a dummy for the post-consulting intervention period, and the interaction of these two dummies. Column 6 in Panel A shows results for the corresponding regression with the mean daily wage bill as the outcome variable. Panel B displays the raw data, where the means in Columns 1, 2, 4, and 5 are the observations used in the regressions in Panel A.

26
_8_

# **The Demographic Pattern**

Key demographic features for pension considerations are:

- A relatively young work force which will experience significant aging;

- Increasing life expectancy of the general population, which will increase the duration of retirement from 15 to 20 years by 2025, based on the current retirement age of 55;

- Declining fertility, with the largest age cohorts about to enter the work force and subsequent cohorts becoming smaller.

Together, these trends imply that the size of retiree population relative to the workforce, or the dependency ratio, will increase significantly in the next thirty years. The dependency ratio is projected to rise from about 17 percent today to about 30 percent by 2025 at the current retirement age.

The population of Indonesia is currently (1995) estimated to be 195 million and projected to be 261 million in 2025. One third of the population is under the age of 15, while 10 percent is over the age of 55. Today's largest cohort, which is of the age 10 - 14, will continue to be the most populous group with the projected decline in fertility. The age distribution of the population, and the expected movement in this distribution for the next 30 years, are shown in the chart below.

<!-- Start of picture text --> Projected Age Distribution<br>for the General Population of Indonesia<br>25000<br>200001 x<br>1995<br>15000 I N, 2005<br>" < > ~~~~~~~' ix \ +~~~~- 2005<br>0 A. ~~~~~~~ - -~~ -A-... 2015<br>10000 5<br>5000<br>0-4 10-14 20-24 30-34 40-44 50-54 60-64 70-74<br><!-- End of picture text --> Source: Demographic Institute, Faculty of Economics, University of Indonesia The size of the labor force is currently 86 million ( 44 percent of the population.) It is expected to grow, both in absolute number and also as a share of the population. By 2025, the work force is expected to be 153 million or 59 percent of the population. Formal sector employees, the initial
lack any treatment by the IHC program, the post-treatment RMSPE for the placebo firms is a good indicator of how well SCM forecasts the outcome variable. The results are reported in Table 13. Dube and Zipperer suggest that the preferred set of conditioning variables should be those that produce the smallest post-treatment RMSPE. By this metric, the benchmark set of conditioning variables is the set that performs best for each of the five outcome variables.

**<u>Table 13.  Forecast Accuracy among Placebo Firms</u>**

||RMSPE for p<br>|lacebo firms<br>||
|---|---|---|---|
||Pre-treatment|Post-treatment|95% CI for population|
|**(**<br>|**log) Median Clear**<br>|**ance Time**<br>|medianof forecast error<br>|
|Benchmark|0.356|0.629|(-0.008, 0.062)|
|Matchallpre-treatment outcomes|0.271|0.631|(0.002, 0.080)|
|Match average pre-treatment<br>outcomes|0.420|0.651|(-0.028, 0.017)|
|**(log)**<br>|**Clearance time - 7**<br>|**5**<sup>**th**</sup>**percentile**<br>||
|Benchmark|0.405|0.740|(-0.049, 0.068)|
|Matchallpre-treatment outcomes|0.291|0.772|(-0.040, 0.073)|
|Match average<br>pre-treatment outcomes|0.485|0.793|(-0.137,-0.026)|
|**(log)**<br>|**Clearance time - 9**<br>|**0**<sup>**th**</sup>**percentile**<br>||
|Benchmark|0.482|0.917|(-0.076, 0.057)|
|Matchallpre-treatment outcomes|0.328|0.961|(-0.089, 0.043)|
|Match average<br>pre-treatment outcomes|0.565|0.966|(-0.150, 0.008)|
||**Inspection ra**|**tes**||
|Benchmark|0.053|0.090|(-0.009, 0.001)|
|Matchallpre-treatment outcomes|0.040|0.094|(-0.011, 0.004)|
|Match average<br>pre-treatment outcomes|0.062|0.098|(-0.010, 0.001)|
||**(log) Import v**|**alue**||
|Benchmark|0.340|0.564|(-0.042, 0.033)|
|Matchallpre-treatment outcomes|0.247|0.584|(0.007, 0.106)|
|Match average<br>pre-treatment outcomes|0.419|0.587|(-0.040, 0.001)|

Note: For each set of conditioning variables, the table reports root mean square prediction errors for pre- and post-treatment periods (columns 2 and 3). The table also reports, for each set of conditioning variables the confidence interval for the population median of the forecast error in the 6<sup>th</sup> month of the post-treatment period (column 4).

39
Figure 5: Brazil: 2002–2005–2008 Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100<br>% population<br>GIC 2002�2005<br>GIC 2005�2008<br>10<br>5<br>yearly % growth<br>0<br><!-- End of picture text --> _Authors’ calculation from PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)_

# **_Opportunity and Growth in Brazil: The Results_**

Table 4: Brazil: 2002–2005–2008 descriptive statistics and partition in types

|_Region_|_Race_|_rank_<sup>02</sup>|_sample_<sup>02</sup>|_q_<sup>02</sup><br>_i_|_μ_<sup>02</sup><br>_i_|_rank_<sup>05</sup>|_sample_<sup>05</sup>|_q_<sup>05</sup><br>_i_|_μ_<sup>05</sup><br>_i_|_rank_<sup>08</sup>|_sample_<sup>08</sup>|_q_<sup>08</sup><br>_i_|_μ_<sup>08</sup><br>_i_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Northeast|black-mixed|1|91118|0.2227|516.73|2|97846|0.2229|550.09|1|93547|0.2272|695.64|
|Northeast|indigenous|2|299|0.0007|576.47|6|309|0.0006|702.42|2|398|0.0010|715.49|
|North|black-mixed|3|25874|0.0381|631.47|3|35053|0.0542|604.64|3|33200|0.0556|769.59|
|South|black-mixed|4|10121|0.0270|683.06|7|11549|0.0292|748.19|6|12006|0.0319|937.98|
|Southeast|black-mixed|5|42007|0.1448|768.61|9|48800|0.1606|806.90|8|47725|0.1633|969.41|
|Center-west|black-mixed|6|16052|0.0300|777.33|8|17223|0.0306|799.66|10|17472|0.0321|1006.28|
|Center-west|indigenous|7|154|0.0003|806.05|1|136|0.0002|444.41|4|175|0.0003|859.83|
|Northeast|white-east asian|8|42720|0.1094|821.07|10|42911|0.1017|823.36|9|40880|0.1018|975.68|
|South|indigenous|9|119|0.0002|866.19|5|128|0.0003|628.87|7|183|0.0005|940.65|
|North|indigenous|10|98|0.0002|879.60|4|206|0.0002|622.59|5|236|0.0003|861.65|
|North|white-east asian|11|9916|0.0146|970.79|11|11088|0.0167|903.47|11|9942|0.0164|1102.10|
|Southeast|indigenous|12|117|0.0004|1082.98|12|105|0.0004|1011.33|12|153|0.0005|1192.87|
|South|white-east asian|13|49021|0.1311|1169.46|14|49133|0.1244|1229.42|14|44957|0.1198|1456.16|
|Center-west|white-east asian|14|12717|0.0244|1179.96|13|13147|0.0238|1176.54|13|12642|0.0236|1433.06|
|Southeast|white-east asian|15|66055|0.2561|1385.93|15|62412|0.2341|1387.16|15|59065|0.2255|1613.84|

_Source: Authors’ calculations on PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)._ Types are ranked in ascending order according to the average income at the beginning of each growth period.

As in the first illustration, we begin this analysis with the assessment of growth according to the equality of outcome perspective. The GICs for the two periods considered are reported in Figure 5.

29
**Table 2: NEET Rates for 15- to 24-Year-Olds, by Region**

|**Region**|**NEET**|**rates, latest y**|**ears**|**Female share**|
|---|---|---|---|---|
||**Total**|**Male**|**Female**||
|**World**|**21.8**|**9.8**|**34.4**|**76.9**|
|**Developing countries**|12.1|8.0|16.0|66.1|
|**Emerging countries**|25.2|9.6|41.8|80.3|
|**Developed countries**|13.1|11.3|14.9|55.7|
|**_Northern Africa_**|26.1|16.7|36.0|67.6|
|**_Sub-Saharan Africa_**|15.5|11.2|19.0|61.4|
|**_Latin America and the Caribbean_**|19.4|11.9|27.0|68.6|
|**_Northern America_**|16.3|14.1|18.6|55.8|
|**_Arab States_**|18.2|9.9|27.1|71.8|
|**_Eastern Asia_**|3.7|2.8|4.7|61.8|
|**_South-Eastern Asia and Pacific_**|18.0|13.4|22.6|61.5|
|**_Southern Asia_**|28.6|5.8|53.3|89.5|
|**_Northern, Southern and Western Europe_**|12.3|12.2|12.4|49.2|
|**_Eastern Europe_**|15.6|13.8|17.4|54.5|
|**_Central and Western Asia_**|23.4|14.8|32.1|67.5|

_Source_ : International Labour Organization, 2017. _Notes_ : the table shows the NEET rate in different regions of the world, using youth population-weighted averaging. The number of countries with available data in different regions are as follows: World (98), developing countries (12), emerging countries (46), developed countries (40), Northern Africa (3), Sub-Saharan Africa (16), Latin America and the Caribbean (16), North America (2), Arab states (5), Eastern Asia (4), South-Eastern Asia and the Pacific (8), Southern Asia (6), Northern, Southern and Western Europe (27), Eastern Europe (7), and Central and Western Asia (4). The most recent year is 2015, with 67 observations. There are 15 observations for 2014 and 16 observations for 2009–2013.

Figure 2 narrows the focus to Morocco and other Middle East and North Africa region countries. The MENA region, in particular the North African part, seems to perform worse than many other regions both in the developed and developing world. Although its peers are not doing particularly well, Morocco has even higher NEET rates than most. In 2017, Morocco was the worst performer among MENA countries that were not in a situation of conflict or state fragility (in other words, excluding the Republic of Yemen, Iraq and the West Bank and Gaza). Its NEET rate is two percentage points above that of the Arab Republic of Egypt, and four and five points higher than that of Tunisia and Algeria, respectively.

7