rafmacalaba/datause-encoder-data huggingface dataset:
manufacturing activities: agriculture has a higher input coefficient for _c-fuel_ than most of the manufacturing activities. However, this aggregation is not misleading since production of fertilizers is energy-intensive and, accordingly, fertilizer prices tend to be highly correlated with energy prices (see e.g. Huang, 2009).

**Table 11. External shock and transfer scenarios: Annual macro growth (%)**

||**F**|**inalyear**<br>||||
|---|---|---|---|---|---|
|**Indicator**|**2012**|**base**|**fuelpwm**|**remitdecr**|**trnsfrpoor**|
|Absorption|99,595|3.9|3.4|3.2|3.8|
|Consumption - private|65,095|4.2|3.7|3.6|4.1|
|Consumption - government|13,307|1.5|1.5|1.5|1.5|
|Fixed investment - private|19,224|4.5|3.5|2.9|4.3|
|Fixed investment - government|1,969|4.1|3.6|3.3|3.9|
|Exports|35,280|4.4|4.6|4.6|4.1|
|Imports|63,874|3.8|3.4|3.3|3.7|
|GDP at factor cost|59,315|4.3|4.1|4.0|4.3|
|Total factor employment (index|n.a.|2.4|2.1|2.1|2.3|
|Total factor productivity (index)|n.a.|1.9|2.0|1.9|1.9|
|Real exchange rate (index)|n.a.|0.2|0.3|0.2|0.3|
|Headcountpovertyrate(%)|21.4|9.5|12.3|10.7|9.2|

Note: Columns other than 2012 show results for 2020.

**Table 12.  External shock and transfer scenarios: GDP shares by sector (%)**

||**2012**|**base**|**fuelpwm**|**remitdecr**|**trnsfrpoor**|
|---|---|---|---|---|---|
|Agriculture and other primary|10.3|9.4|8.6|9.5|9.4|
|Industry|24.3|23.2|23.9|23.3|22.8|
|Industry -manufacturing|16.4|14.9|15.8|15.3|14.5|
|Industry -other|7.9|8.3|8.1|7.9|8.3|
|Services|65.4|67.4|67.6|67.2|67.8|
|Services - private|53.3|53.6|53.6|53.4|54.0|
|Services - government|12.1|13.8|13.9|13.8|13.8|
|Total|100.0|100.0|100.0|100.0|100.0|

Note: Columns other than 2012 show results for 2020.

As the shocks in this section are assumed unchanged growth in real government consumption and investment at the same time as GDP growth contracts and, for one of the scenarios, the government raises spending in one areas (transfers), we see that the share of government of GDP rises in all of them (see table 13). However, it is noteworthy that, even though our transfers rise substantially under _trnsfrpoor_ – from 15.3 to 18.5 percent of GPD, the economic growth is not hurt particularly much.

Figure 19 in turn shows that the increase in transfers clearly benefits the retired and those dependent on social benefits – and marginally even farmers - while the consumption growth of the other households is marginally dented. When fuel prices rise and the economy slows down, 30
questionnaire, administered in Kiswahili, filled out in class by students enrolled in grades 6, 7 and 8 in the spring of 2005. Questions were drawn from the national curriculum. The KAP survey did not ask questions about condoms but did include an open-ended question about how HIV/AIDS can be prevented, and we recorded the frequency with which condoms were mentioned.

# • **Childbearing**

Because data on self-reported behavior may be contaminated by reporting bias, our main measure of whether the programs changed the probability that children engaged in risky behavior is the incidence of pregnancy and childbearing among girls in our sample. In addition to being an important outcome in its own right, in this primary-school-age population, childbearing is a proxy for risky sexual activity, as unprotected sex can lead to both pregnancy and HIV/AIDS.

Information on childbearing and marital status was obtained for all girls in the sample during six visits to each sampled primary school in the 36-month period following the intervention. At each visit, the list of all students in the baseline sample was read aloud to pupils enrolled in upper grades at the time of the visit, and for each of the baseline students the following questions were asked: Is “Mary” still in school? If yes, in what grade? In what school? Does she still live in the area? Is she married? Does she have any children? If so, how many? Is she pregnant?

Checks suggest this technique of collecting childbearing and marital outcomes generates accurate data. Among a sub-sample of 282 teenage girls that were tracked at their home and interviewed, 88% of those who were reported as having started childbearing by their former schoolmates had indeed started childbearing, and 92% of 14
- 19 -

**Table 8: Main Determinants of MWRs**

**Dependent Variable: 100*MWR; Least Squares with Robust Standard Errors YEAR=1999; Observations: 937**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|C|60.86516|2.100710|28.97362|0.0000|
|AGE|0.406372|0.023248|17.47959|0.0000|
|LOG(PREMIUM)|1.854153|0.227955|8.133850|0.0000|
|GUARANTEE|-0.136101|0.023538|-5.782169|0.0000|
|DEFERMENT|-0.006032|0.022320|-0.270278|0.7870|
|Male|1.639658|0.522999|3.135107|0.0018|
|Female|4.316622|0.286877|15.04697|0.0000|
|R-squared|0.407434|Mean dep|endent var|97.81531|
|Adjusted R-squared|0.403611|S.D. depe|ndent var|4.899386|
|S.E. of regression|3.783611|Akaike inf|o criterion|5.506677|
|Sum squared resid|13313.61|Schwarz|criterion|5.542855|
|Log likelihood|-2572.878|F-sta|tistic|106.5742|
|Durbin-Watson stat|1.903532|Prob(F-s|tatistic)|0.000000|

**Table 9: Main Determinants of MWRs Dependent Variable: 100*MWR; Least Squares with Robust Standard Errors YEAR=2002; Observations: 1,517**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|C|65.91515|1.296474|50.84185|0.0000|
|AGE|0.499496|0.016820|29.69642|0.0000|
|LOG(PREMIUM)|1.862513|0.144434|12.89526|0.0000|
|GUARANTEE|-0.118013|0.016261|-7.257291|0.0000|
|DEFERMENT|0.025761|0.015423|1.670247|0.0951|
|Male|1.352054|0.394186|3.429992|0.0006|
|Female|4.419103|0.179380|24.63540|0.0000|
|R-squared|0.536716|Mean dep|endent var|107.9591|
|Adjusted R-squared|0.534875|S.D. depe|ndent var|4.709875|
|S.E. of regression|3.212139|Akaike inf|o criterion|5.176355|
|Sum squared resid|15579.93|Schwarz|criterion|5.200924|
|Log likelihood|-3919.265|F-sta|tistic|291.5563|
|Durbin-Watson stat|1.614660|Prob(F-s|tatistic)|0.000000|
# **<<A>>** _I. POLICY INDICATORS AND STYLIZED FACTS_

We employ two different indicators of agricultural and food policies: the nominal rate of assistance ( _NRA_ ) to agriculture and the relative rate of assistance ( _RRA_ ), both from the World Bank’s agricultural distortions database (see Anderson and Valenzuela 2008, for calculation details). This database reports the most consistent and comparable estimates of agricultural protection across countries and over time. In our econometric analysis, we use a sample of 74 countries, comprising yearly data from 1955 to 2005 (see table S.1 in the supplemental appendix, available at http://wber.oxfordjournals.org/). The average number of observations per country is 35. We work with an unbalanced panel with more than 2,600 observations.

The _NRA_ measures total transfers to agriculture as a percentage of the undistorted unit value. The _NRA_ for agriculture is obtained as a weighted average of the _NRA_ at the product level, using the undistorted value of production as a weight. The _NRA_ is positive when agriculture is subsidized, negative when it is taxed, and zero when net transfers are zero. The _NRA_ includes both the assistance provided by all tariff and nontariff trade measures applied to agricultural products and any domestic price-distorting measures.<sup>1</sup> The price equivalent of any direct intervention regarding inputs or outputs is also included.<sup>2</sup> To account for the protection of manufacturing sectors, which is an important source of indirect taxation on agriculture, especially in developing countries, we use both the _NRA_ and the _RRA_ , which is calculated as the ratio of agricultural _NRA_ to nonagricultural _NRA_ .<sup>3</sup> The _RRA_ is a useful indicator for international comparisons of antior proagricultural policy regimes. There are fewer observations for the _RRA_ because the country and time-series coverage is smaller than for the _NRA_ . Specifically, the _RRA_ data contain five fewer countries (69 instead of 74).

4
Tables A.18. Approval of women’s work under different contexts, by sub-groups **Table 18.1 Approval of women’s work under different contexts, non-working vs. working**

||Workin<br>Mean<br>|g women<br>/(SE)<br>|Non-work<br>Mea<br>|ing women<br>n/(SE)<br>|Pairwi<br>differen<br>|se t-test Mean<br>ce<br>|
|---|---|---|---|---|---|---|
||**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|
|**A woman's characte**|**ristics and ro**|**les**|||||
|She is married|0.99|0.95|0.98|0.94|-0.01|-0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|||
|She has small|0.90|0.80|0.84|0.72|-0.06***|-0.09***|
|children under 5<br>who can be<br>looked after by<br>familymembers|(0.01)|(0.02)|(0.02)|(0.02)|||
|She has small<br>children under age|0.32<br>(0.02)|0.20<br>(0.02)|0.21<br>(0.02)|0.12<br>(0.01)|-0.11***|-0.08***|
|5, who she will<br>need to drop with<br>non-family<br>members while<br>she is at work|||||||
|She is unable to|0.52|0.39|0.41|0.30|-0.11***|-0.10***|
|keep up with all|(0.02)|(0.02)|(0.02)|(0.02)|||
|household tasks<br>|||||||
|without help|||||||
|She is well|0.98|0.98|0.98|0.97|-0.00|-0.01|
|educated|(0.01)|(0.01)|(0.01)|(0.01)|||
|**A woman's husband**|**'s employme**|**nt status**|||||
|Her husband is|0.97|0.94|0.94|0.94|-0.02*|-0.00|
|not working and has<br>no income|(0.01)|(0.01)|(0.01)|(0.01)|||
|Her husband was|0.95|0.84|0.89|0.78|-0.06***|-0.06***|
|previously<br>unemployed [e.g.,<br>no income] but<br>has now found a<br>job|(0.01)|(0.02)|(0.01)|(0.02)|||
|Her husband is a|0.94|0.91|0.91|0.866|-0.030*|-0.04**|
|migrant worker<br>and is not sending|(0.01)|(0.01)|(0.01)|(0.014)|||
|her money|||||||
|**A woman'sjobs' ch**|**aracteristics**||||||

53
probabilities by age. We also notice that a high rate of economic growth makes the lines quite steep, indicating that small changes in the IRR on contributions can deviate the system from its long-term equilibrium. As a corollary, for a given IRR, a small change in the underlying macro and demographic conditions can also divert the system from equilibrium. Clearly, outside the steady state the sustainable IRR would need to change at each point in time. The question is how.

**Figure 1:  Financing Gap and the IRR**

<!-- Start of picture text --> 20<br>Retirement GDP grows at GDP at 5%,<br>age starts at 5% per year population at<br>15 55 with 10% 2.5%, and<br>probability retirement<br>GDP growts at age starts at<br>10 5% and 55 with 10%<br>population at probabiliy<br>2.5%<br>5<br>0<br>0.01 0.02 0.03 0.04 0.05<br>-5<br>Population growth<br>rate at 4%<br>-10<br>Survival probabilities<br>of year 2050<br>-15<br>Baseline<br>-20<br>NPV(Balances)/WageBill<br><!-- End of picture text --> Note: The baseline scenario is built over the following assumptions: population growth of 1% per year; GDP growth of 3% per year; 2% growth in wages between ages; survival probabilities for Morocco in 2004; minimum retirement age 40 with 1% probability and maximum retirement age 75; no dropouts within age cohorts. The other lines represent deviations in one of the parameters from the baseline. Source: Authors’ calculations The proposal developed here is based on an accounting framework “a la Swedish” -- although as shown below the actual indexation mechanism used in Sweden is not fully consistent with this underlying framework. The idea is simple: choose the IRR at each time _t_ in a way that guarantees that the liabilities of the pension system are equal to its assets. The trick then is to properly define these liabilities, and in particular, the assets of the ER pension system with pay-as-you-go financing.

The assessment of the liabilities of the system is straight forward. They are given by the present value of future pension payments to current retirees and to current contributors based on rights accrued to date.<sup>8</sup> In the case of a notional account system the value of the IPD is easily defined. It is given by the capital accumulated in the 8 The pension system liability definition here corresponds to the “gross implicit pension debt I” definition of Holzmann, Palacios and Zviniene (2001).

6
# **4.2 Long-run Effects on Ebola Outcomes**

Roughly one year after our endline survey, the first confirmed Ebola case was recorded in Sierra Leone. We turn now to examining the longer-run effects that the interventions had on reporting and mortality during the ensuing epidemic.

The treatment effects on reported Ebola cases are apparent in Figure 3: the left panel presents the sum of total reported cases in each week by treatment arm; the right panel is the cumulative count of cases during our study period. Between September 2014 and May 2015, we count 515 total cases in control sections; yet in sections with clinics receiving the CM and NFA interventions 735 and 795 cases are reported, respectively. This difference is even more striking for confirmed cases: only 21 confirmed cases are reported in the 54 control sections, while 248 are reported in the treated sections (see Figure E.2).

**Figure 3:** Total Cases by Treatment

<!-- Start of picture text --> C CM NFA G C CM NFA<br>C 800<br>40<br>30<br>20<br>10 600<br>0<br>CM G G<br>G G<br>40 G<br>30 400 G G<br>20 G G<br>10 G<br>0 G G G G<br>NFA 200 G G<br>40 G G G G G<br>30 G<br>2010 0 G G G G G G G G G G G G<br>0<br>Sep Oct Nov Dec Jan Feb Mar Apr May Sep Oct Nov Dec Jan Feb Mar Apr May<br>2014 2014 2014 2014 2015 2015 2015 2015 2015 2014 2014 2014 2014 2015 2015 2015 2015 2015<br>(a) Weekly Counts (b) Cumulative Counts<br>Total Cases<br>Cumulative Total Cases<br><!-- End of picture text --> Figure 3(a) plots the time series of total cases by week; bars represent the raw counts. We use the date that the case was first saved in the VHF. Figure 3(b) graphs the cumulative count of total cases by treatment group.

We report regression results using Equation 3 in Table 3. In the top panel, the outcomes are the raw counts of total, confirmed, and negative cases. The pooled effect implies a 62 percent increase in the average number of total cases. The effect is smaller and less precisely estimated for NFA ( _p_ = 0 _._ 13), which is consistent with our medium-run results, which also show smaller effects on utilization for NFA clinics, especially in the Ebola sample (see first row of Table 5). Nonetheless, we cannot reject the null hypothesis 27
# A **Survey** of **Recent Batimteu** of Price **Elasticities** of **Demand** for Transport

## TABLE OF CO6N1TS

||Page|
|---|---|
|I.<br>Introduction............<br>.<br>....<br>......<br>.<br>..................|...<br>I|
|II.<br>Sources of Demand Studies<br>..........<br>..............<br>. .|1|
|III.<br>Concepts of Elasticities...............<br>.............<br>. . . . .|2|
|XV.<br>Estimates of Price Elasticities<br>.<br>....**<br>..........<br>**..|9|
|V.<br>Conclusion.....<br>..<br>.<br>.. .<br>...<br>..<br>. ...|13|
|FigureI1...........0....e......<br>.....|15|
|Tables||
|1<br>Surin-ry<br>Statistics<br>.............................<br>.16||
|2.<br>Elasticities of Demand for Passenger Transport .......|17|
|3.<br>Elasticities of Demand for Freight Transport .........|18|
|4.<br>Elasticities of Demand for Gasoline .................|. 19|
|5.<br>Selected Estimates of Cross Elasticities .............|20|
|References.......<br>..<br>.<br>......<br>........|21|
|Appendix 1 List of Journals Scanned<br>.<br>.<br>...|23|
|Appendix 2 Complete List of Elasticity Estimates<br>.......<br>...|24|
|Appendix 3 Inferring Ordinary Demand Elasticities for<br>Mode Choice Elasticities<br>..<br>.<br>......|25|
|Annex A<br>An Annotated Bibliography of Recent Estimates of<br>Price Elasticities of Transport Demands..<br>.....|A.1|
|Bibliography for Annex A.........<br>.<br>.............|A.18|

This paper was prepared as part of a project on Pricing, Cost Recovery and Efficient Resource Use in Transport. It was prepared hy Tae H. Oum, W.G. Waters II and Jong Say Yong from the Center for Transportation Studies, University of British Columbia under the direction of Ian G. Reggie, INUTD.
unemployment spells is associated with high individual and social costs. It tends to cause the erosion of human capital, and often leads to poverty and social exclusion. Accordingly it is the high long-term unemployment rates prevailing in ECA transition economies which are of particular policy concern.

# **High unemployment among less skilled workers**

Unemployment in the European transition economies is heavily concentrated among less educated workers. Generally, the unemployment rate is the higher, the lower is the educational level (Table 6). Unemployment rates among workers with primary and lower secondary education tend to be extremely high, usually well above 20 percent. They are significantly lower – although often still high -- for workers with upper secondary education, on average around 13 percent. The unemployment rate drops dramatically to 4 percent for workers with university education (the CEE average). It is noteworthy that the unemployment rate for university educated workers tends to be low even in high unemployment countries, such as Bulgaria (8.5 percent) or Poland (5.6 percent), suggesting that the fall in labor demand has been biased against the less skilled workers. A notable exception is FYR Macedonia, where the unemployment rate is very high even among workers with tertiary education (16.6 percent), although still half the overall unemployment rate. This exception notwithstanding, university education seems to be the best protection against unemployment in the European transition economies. Unfortunately, lack of relevant data makes it impossible to ascertain whether or not this result holds also for other ECA countries.

**Table 6 Unemployment hits less educated workers particularly strong**

||Less than<br>upper<br>secondary|Upper<br>secondary|Tertiary|
|---|---|---|---|
|**CEE**||||
|Bulgaria|33.1|19.4|8.8|
|Czech Republic|21.5|7.1|2.5|
|Estonia|18.6|13.3|8.0|
|Hungary|11.2|5.2|1.2|
|Latvia|21.0|13.0|5.5|
|Lithuania|23.1|21.7|10.1|
|Poland|23.9|19.4|5.6|
|Romania|4.0|8.6|3.9|
|Slovakia|42.5|18.7|5.2|
|Slovenia|8.9|5.5|2.3|
|**SEE**||||
|Croatia a)|13.7|16.7|7.6|
|Macedonia, FYR|-|-|-|
|Serbia and Montenegro a)|24.9|19.7|9.5|
|- Data not available||||
|Note: Labor Force Survey<br>data<br>a) 2002||||
|Source:||||
|CEE: Eurostat||||

Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations 25
**Policy, Research, and External Affairs**

**Women** in **Development** WPS 461

'This paper - a product oi' thc Womnen in Developimtent Division, Population and Hlunian Resources Department -- is pairt ol' a larger ef'for in PRE to deteniine if and how womcn's<sup>productivity (and Illthus</sup> family welfarc) are improvedwhen womcn are givenmore accesstoeducation, extension,<sup>training, credit,</sup> health care, and other public resources. Copics are available free from the World Bank,<sup>1818 If Street NV,</sup> Washington DC 2(033. Please contact Bclinda Smith ,room S9-125. extension 35 108 (51 paces<sup>with</sup> tables).

Using household survey data t'rom Peru, Khandkcr estimates dit'fercnces between male and femalc participation in the labor market, productivity (measured by wages), and econiomic returns to schooling.

fIe trics to identify characteristics that enable some **women,** althoughl not manv, to participate in the labor market; to determine whether the private retumns ltO eucaLion vary by genider andi influcnce sch0ool einrollmiint: and to evaluate thc extentt to \Alhich thc male-emrale wA age _gap_ is caused b\ dil'f'ernces in humnan capital.

KhandkQr reaches threc poe licy conclusions: Public s,chools are less etfcfcti\ e than private schools ini raising producti i tI and rectucinlg the w.agoe g;ap. Policmnt akers shlouldt manke thle public slchool systcnm Imore eflfective.

* Investments in education and training for girls increase their participation anti productivity in the labor market more than a similar investnerit in boys' education incrcases dicirs. T'hose investments also reduce fertility andi improve the education of children and the health and nutiritionl ol' all f;amilv members. Returns are high on - human capital investments in women at lea;st as high as an equivalent investment in mein. 'I'hc governmcnt must identify ways to chonncl imore resources to women's education.

* Ilousehiolds anid communities are probahi the main sources of enider bias in parental invest-inent in child(irel's Lducation. so tlh oo\ eminiicit ne ust i(elti I ' As s to i Il IucIcL the household's diccisionls about education. Policv re'C;arch is needed to identil\ hoA households and commuilnuiities aflect parental diecisioins and hoA thc government Calln intcr\en c c _ti\ elv_ to aif'ct t fis decisionm aking.

IP. WIIIi' I P,ipic **.q.r** tlS'cnun, itw ' ien **ef** o!gs Ark urwtdr **Aa** in tiro **Banik** l 'Polh,, Rk,C,irthi, MTSl IL\trAtl AffairI (om i;bx An 0 ti% Cot O) C **'r;** 11 it .! _gt I_ fIitni ', Li t qlliukl\ . _e'1_ i **I)I ;ic** ICT1: I **_tim_ le I** tcs lUhtl I'llt X$ff~~~~~~~! rFf( ,!,11< 7il!:.! i t' >;T 'OT,)>t^sr rCj1r-'*%.1' 1T. 11.1lk **1-11!** I't.~~~~~~~~~~I iR.I.,\u.itV< cr.t:t