rafmacalaba/datause-encoder-data huggingface dataset:
Along with Finland and Norway, Sweden is among the nine countries in the sample that require employers to give priority to workers based on their seniority, marital status or other specific criteria—and also among the nine countries that require employers to retrain redundant workers before they can be dismissed (see Table 4). Sweden’s priority rule is based on the seniority principle: the employee hired last will be dismissed first. This last-in-first-out principle was changed in 2001 to allow employers with 10 or fewer employees to exempt two employees from the seniority rule (von Below and Thoursie, 2008). Again along with Finland and Norway, Sweden is one of the eight economies in the sample that require employers to first offer available positions to workers previously dismissed for redundancy. Thus it can be said that Sweden’s notification rules are more relaxed while its priority rules are stricter than those of the majority of the other 19 countries in the EU15, G7 and Nordic region.

The mandatory notice period and severance payment together make up the redundancy cost measure, expressed in weeks of salary, which is calculated as the average for workers with 1, 5 and 10 years of tenure. Sweden’s mandatory notice period, at 14.4 weeks of salary, is the second highest in the sample after Luxembourg’s, at 17.3 weeks of salary (see Table 4). The average notice period is only 6.9 weeks in the EU15, 5.6 in the G7 and 7.2 in the other Nordic countries. Denmark and the United States require no notice before dismissing a redundant worker.

Figure 9: The cost of making a worker redundant in Sweden is higher than the cost in all other Nordic countries and higher than the OECD, EU15 and G7 averages <!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Redundancy cost (weeks of salary), 2013 Portugal Luxembourg Germany Spain Greece Sweden OECD average Ireland EU15 average France Iceland Finland Canada G7 average Norway Netherlands United Kingdom Belgium Nordic average Italy Japan Austria Denmark United States<br><!-- End of picture text --> Source: World Economic Forum (2014), based on Doing Business 2014. Redundancy cost is the average mandatory notice period and severance payment for workers with 1, 5 and 10 years of tenure. The Nordic average excludes Sweden.

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6 Background

# Research on Single-Sex Tertiary Education

In a summary of the results of a large longitudinal study of more than 200,000 students in over 300 American colleges, Alexander Astin concluded that "Single-sex colleges show a pattern of effects that is almost universally positive" with respect to a large array of academic and pre-professional outcomes.<sup>11</sup> / Discussing the finding that effects of single-sex schooling were generally stronger for women than men, Astin speculated that such effects were probably due to the more circumscribed heterosexual activity and to a greater sense of identification and communal feeling when both students and faculty are predominantly of the same sex. Other studies have identified undergraduate education in a women's college as a major explanatory factor for women's career achievement patterns.!U/ Why does single-sex education produce beneficial effects.for women? According to Tidball and Kistiakowsky, women's colleges convey a sense of being. in an environment where there are many other women seriously involved in a variety of academic pursuits. The presence of a high proportion of female faculty is another frequently cited factor.

Honever, tertiary single-sex education has occurred almost exclusively in the private -- as opposed to public -- sector, and research on its effects has not adjusted results either for the greater affluence of students in private colleges, nor for organizational or resource differences between colleges. Further, it has been suggested that these studies failed to consider the possibly greater career and academic motivation that might also be associated with attendance at private and exclusive American women's colleges.11./
we did not account for openly unemployed workers, of either variety. In practice, however, surveys may report a head of household as being unemployed (and therefore with no declared wage income), while at the same time receiving non-wage income from, say, holdings of financial assets and government transfers. Instead of simply assuming that the rate of growth of income or consumption is zero for the unemployed (an assumption that may lead to unrealistic results for medium-term exercises), our inclination would be to treat these observations as follows. For unskilled workers, it would seem reasonable to assume that the openly unemployed are actually employed in the informal sector or the nontradable agricultural sector (even if they don't declare it), depending on the sector of occupation, at the going wage. One can thus apply the growth rates of consumption and "full" disposable income taken from the macro component of IMMPA for that category of workers, as in the sequence described previously. By contrast, for skilled workers, the assumption that the unemployed are actually working in the informal sector may not be very satisfactory, for the reasons discussed earlier. We would suggest using the growth rate of pre-tax, non-wage income only, for that category of workers, and adjust the rate of growth of consumption taken from the macro component of IMMPA in proportion to the differential between disposable income of employed workers and pre-tax non-wage income.

Finally, it should be noted that we have abstracted in the above discussion from issues associated with differences between national accounts data (on which IMMPA is based) and survey data (from which poverty measures are calculated). However, in practice, large discrepancies can arise between these two sets of data. In particular, it is possible that the composition of employment, output, and the inter-group income distribution generated by IMMPA and the household survey (following step 1 above) are different.<sup>3 5</sup> Indeed, it is well recognized that national accounts and survey estimates of income and consumption patterns can differ significantly. Moreover, these differences may even be increasing in some cases, as appears to be the case in India (see Deaton (2001)). In general, the evidence suggests that nominal consumption growth rates estimated from survey data tends to be substantially lower than 35As indicated earlier, for the simulation exercises reported in this paper, we generated an artificial sample (based on a log-normal distribution) using the mean per capita real income data generated by the initial calibration of the model. The inter-group Gini coefficients that are calculated from the model and from the household survey are thus very close, given the relatively large number of replications. In practice, however, this may or may not be the case.

57
16 set that was used to calibrate the consumption model. Only specifications that are able to predict actual observed consumption levels well should be used to impute consumption into later rounds of survey data.

The simulation based SAE technique deployed here has some attractive features that are woth noting.<sup>10</sup> First, the approach of estimating consumption rather than estimating final welfare indicators directly allows the estimation of any number of poverty or inequality indicators with the same simulation.<sup>11</sup> Second, it provides consistent estimates of both the mean _and_ the variance of consumption, and thus also a consistent estimate of the welfare measure in the future.<sup>12</sup> Third, while the SAE method does impose some structure on the distribution of the idiosyncratic error term in the consumption model, the heteroskedasticity model applied within this approach permits a partial update of the distribution of the error term over time, reducing prediction error due to assumed stationarity of the error term.<sup>13</sup> Fourth, the technique is convenient to implement given the freely and readily downloadable PovMap2 software.<sup>14</sup>

> 10 To be sure, the small area estimation approach is just one of a variety of valid approaches that can be employed (see for example, Azzarri et al. (2006), Tarozzi and Deaton (2009), and Matthiasen (2009)). 11 This is in contrast to approaches, such as Deaton and Tarozzi (2009) or Tarozzi (2007), which require the estimation of different first stage models for different welfare indices.

> 12 Azzarri et al. (2006) use only<sup>_x_</sup> _t_<sup></sup>  _k_ <sup>ˆ</sup> _t_ to predict ln<sup>_c_</sup> _t_  _k_<sup>. Yet consistent estimation ofˆ</sup> _t_<sup>is not sufficient</sup> for the estimation of _W(ct+k.)_ which is a function of _ct+k_ , and not a function of the distribution of conditional expectation<sup>_x_</sup> _t_<sup></sup>  _k_  _t_ ` . For this reason, once<sup>ˆ</sup> _t_<sup>has been estimated within the SAE approach, an error term</sup> ^ _u t_  _k_ is randomly drawn and added to<sup>_x_</sup> _t_<sup></sup>  _k_ <sup>ˆ</sup> _t_ to recreate the conditional distribution of _lnct+k_ . Otherwise, the variance of the distribution of _lnct+k_ , is biased, resulting in a biased estimate of _W(ct+k.)._

> 13  Only the estimated parameters of the consumption variance equation are assumed to be stable over time, while the consumption variance predictors are allowed to change.

> 14 <u>http://iresearch.worldbank.org/PovMap/PovMap2/PovMap2Main.asp.</u>
|Household Income (PKR)|‐80.719*|‐4,667|28,408*|18,923|
|---|---|---|---|---|
||[0.0788]|[0.9347]|[0.0986]|[0.2511]|
|Age|4.4560|2.9001|0.2908|0.4213|
||[0.2541]|[0.3958]|[0.8458]|[0.7825]|
|No. of Dependents|1.1282|3.2667|0.9970|0.6212|
||[0.6503]|[0.2114]|[0.4501]|[0.6077]|

Columns show difference in means between treatment and control group p‐values in brackets

*** p<0.01, ** p<0.05, * p<0.1

Source: Saraf, 2019; World Bank Group, 2018 33
TABLES & FIGURES

# **Table 2 - Impact of rainfall shocks on total per capita expenditure**

||(1)|(2)|(3)|
|---|---|---|---|
|Positive rainfall shock|0.004|0.016|0.020*|
||(0.012)|(0.012)|(0.011)|
|Negative rainfall shock|-0.031**|-0.043***|-0.040***|
||(0.014)|(0.014)|(0.014)|
|Female HH head|||0.009<br>(0.006)|
|HH head Age|||0.007***<br>(0.000)|
|Household Size|||-0.110***<br>(0.001)|
|HH Head Education (ref: no education):||||
|•<br>Elementary Undergraduate|||0.145***|
||||(0.013)|
|•<br>Elementary Graduate|||0.277***|
||||(0.015)|
|•<br>High School Undergraduate|||0.369***|
||||(0.017)|
|•<br>High School Graduate|||0.491***|
||||(0.017)|
|•<br>College Undergraduate|||0.749***|
||||(0.020)|
|•<br>College Graduate|||1.203***<br>(0.026)|
|Constant|5.505***|5.531***|5.382***|
||(0.012)|(0.005)|(0.020)|
|Observations|65,355|65,355|65,355|
|R-squared||0.003|0.397|
|Number of mun_id_2011|915|915|915|
|Municipality FE|NO|YES|YES|
|MunicipalityRE|YES|NO|NO|

The estimates control for year dummies. Standard errors are clustered at the municipality level. *** p<0.01, ** p<0.05, * p<0.1 23
**Table 12 Linear Panel Method (Population Average Estimator)**

||Regulations|Finance|Entry|Time to<br>Export|Infrastructure|Property|
|---|---|---|---|---|---|---|
|RealGDP(PPP)|1.004|1.044|1.085|1.057|0.985|1.050|
||(0.031)***|(0.041)***|(0.031)***|(0.032)***|(0.033)***|(0.035)***|
|Log(Area)|-0.119|-0.138|-0.182|-0.152|-0.112|-0.154|
||(0.038)***|(0.043)***|(0.043)***|(0.041)***|(0.038)***|(0.042)***|
|Remoteness|-0.396|-0.408|-0.480|-0.618|-0.565|-0.637|
||(0.168)**|(0.204)**|(0.194)**|(0.182)***|(0.174)***|(0.178)***|
|Export Growtht,t-1|0.036|0.039|0.027|0.031|0.035|0.039|
||(0.018)**|(0.021)*|(0.018)|(0.018)*|(0.020)*|(0.021)*|
|log(TTRI)|-0.118|-0.200|-0.219|-0.170|-0.095|-0.168|
||(0.051)**|(0.064)***|(0.058)***|(0.058)***|(0.033)***|(0.054)***|
|log(MATTRI)|-0.051|-0.171|0.065|0.151|-0.099|-0.155|
||(0.019)***|(0.097)*|(0.045)|(0.086)*|(0.035)***|(0.057)***|
|log(MATTRI)*Indicator|0.054|0.031|-0.012|-0.063|0.055|0.018|
||(0.021)**|(0.026)|(0.004)***|(0.024)***|(0.023)**|(0.008)**|
|Indicator|0.315|0.115|-0.018|-0.145|0.997|0.084|
||(0.065)***|(0.098)|(0.008)**|(0.083)*|(0.153)***|(0.028)***|
|Constant|3.516|2.583|3.439|5.155|4.205|4.576|
||(1.683)**|(1.952)|(1.891)*|(1.884)***|(1.766)**|(1.834)**|
|Observations|205|175|204|203|193|189|
|Number of Countries|98|82|98|97|90|88|

Panel regression. Robust standard errors clustered by country are in parentheses. All regressions control for year fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

22
32 participants themselves are concerned, training measures are valued much more than the previously discussed measures of direct job creation. Whether this is an expression of wishful thinking or not can only be established by linking individual characteristics with training participation on one hand and with outcomes, i.e. labour market status and earnings, on the other hand. Only after one has controlled for observable individual characteristics and, additionally, for selection bias, can one see whether training participation has indeed increased re-employment probabilities. In the Polish case, this is left to later work.

# Job brokerage

The raw data of the supplement also allow us to say something about the placement activities of labour offices. Of those in the August 1994 sample who entered into a new relationship with an employer 6.6% found their last job through a labour office. The majority found a job through personal contacts - i.e. through family and friends - (30.7%), through professional contacts (8.5%), through a direct application to the firm (18%) or through an announcement of an employer (8%). Thus, labour offices seem to be a marginal contributor to job brokerage in Poland as far as the national average is concerned.29 With respect to the unemployed, only 8% were ever offered a job placement in the regular labour market. Of the offered job slots 47% were not taken up by the unemployed. That there are serious problems with the brokerage function of labour offices can be seen from the reasons given for not having taken up the offered job. More than a quarter of the job offers had already been filled when the applicants arrived at the firm (26%). In 14% of the cases the employer asked for qualifications which the applicant did not have, while in 13% of the cases the employer did not want to hire the applicant outright. The probably low quality of many of these job offers might be inferred from the fact that among those who took up the offered job but at the time of the interview had already terminated this work, 33% quit voluntarily.

# 3.2.3. Evaluation of Further Training and Re-training: Estimation of an augmented matching function

To evaluate Polish training measures econometrically an augmented hiring (matching) function is estimated using pooled times series cross section (voivodshiplevel) data. To this purpose several building blocks are needed (cf. Jackman and Haskel, 1988 and Lehmann, 1993b).

29The employment offices set up by the trade unions have an even more dismal record. Only 0.2% of all jobs were brokered by these offices in our sample!
development and growth. Hussein (2002) used savings to GDP as a proxy for financial development where financial savings is the sum of demand, time, and savings deposits held at deposit money banks. He utilized such to assess the response of the savings rate to the liberalization of interest rates in Egypt in early 1991. The other three studies on savings in Egypt focused on gross domestic savings that include both public and private savings.<sup>10</sup> This research fills a significant gap on empirical modeling of private savings in Egypt. It employs a new time series on private savings in Egypt that was compiled by the Central Bank of Egypt (CBE) and the Central Authority for Public Mobilization and Statistics (CAPMAS) in Egypt. This series is inclusive of savings in deposits, money banking, post offices, specialized banks, investment and savings certificates issued by banks in addition to institutional private savings in pension funds and insurance companies. These were significant components that account for almost 15% of aggregate private savings during 1990-2010.

<!-- Start of picture text --> 100%<br>Structure of Aggregate Private Savings in Egypt<br>90%<br>80%<br>70%<br>60%<br>50% Institutional<br>40%<br>Banking<br>30%<br>20%<br>10%<br>0%<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: CBE and CAPMAS, 2014 While the new series is available in annual frequency, the Chow-Lin’s Temporal Disaggregation Methodology (Chow and Lin 1971) is employed to disaggregate the annual data into a higher frequency quarterly series. This methodology generates the best linear unbiased interpolation, distribution, and extrapolation of time series by using a related series called the indicator variable. The Chow-Lin method uses quarterly indicators to generate the quarterly volatility and transforms annual frequency data to quarterly data. To generate a quarterly series of aggregate private savings, the quarterly series of non-government demand and time deposits (obtained from the IMF – IFS 2015) was used as an indicator variable.<sup>11</sup> The generated quarterly series was deflated using CPI and divided by the population count to obtain the real gross private saving per capita in logarithm.

Motivated by the Life Cycle Model, it follows a brief account on relevant variable selection and the hypothesis to be tested with respect to the saving behavior in Egypt.

> 10 See  ElSayed (1993), Touny (2008) and the literature review section.

> 11 Full details of the matlab code and output is provided in the annex.

12
39

- Harrison, Lawrence E., 1992, Who promperg) * how ciltuIral values shape economic and ponitical swrccss, New York, NY: Basic Books.

- Isham, Jonathan, Deepa Narayan, and Lant Pritchett, 1995, "Does participation improve performance: Establishing causality with subjective data," _World Bank Economic Review,_ 9(2):175-200.

- Knack, Stephen and Philip Keefer, 1996 'Does social capital have an economic payoff? A cross-country investigation," mimeo (July).

- La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert Vishny, 1997, 'Trust in Large Organizations," Mimeo.

- Morduch, Jonathan, 1995, "Income smoothing and consumption smoothing," _Journal of Economic Perspectives, 9(3)._

- Narayan, Deepa, 1995, uThe contribution of people's participation in 121 rural water projects,' _ESD Occasional Paper Series No. 1._ World Bank: Washington DC.

- Narayan, Deepa, 1995, "Designing Community Based Development," _Environment Department Papers, Participation Series No._ 7, The World Bank: Washington DC.

- Narayan, Deepa, 1997, Voices of the poor: Poverty and Social Capital in Tanzania," forthcoming ESD monograph series. Word Bank: Washington DC.

- Olson, Mancur, 1996, "Distinguished lecture on Economics in Government: Big Bills Left on the Sidewalk: Why Some Nations are Rich, and Other Poor," _Journal of Economic Perspectives,_ 10(2):1-24.

- Ostrom, Elinor, 1990, Governing the conmmnns the evnlution of institiitionn fnr collective action, Cambridge, England; New York: Cambridge University Press.

- Platteau, Jean-Philippe, 'Behind the market stage where real societies exist (Part I and II)," mimneo.

- Putnam, Robert D with Robert Leonardi and Raffaella Nanetti, 1993, Making demarracy wnrk *Civic traditinns in modern Ttnly, Princeton, N.J. : Princeton University Press.

- Putnam, Robert D. 1995, 'Bowling alone: America's declining social capital" _Journal of Democracy, 6(1)._

Rogers, Everett, 1983, Diffasinn of Tnnnvatinns, New York: The Free Press.
growth.<sup>24</sup> To the extent that openness contributes to growth that increases borrowers' net worth, one would expect these improvements in the real sector to be reflected in the growth of the banking sector. Gertler and Rose argue cogently that, in a world of infornational asymmetry, borrower net worth is at the core of the link between finance and aggregate economic activity.25 Because greater net worth increases a borrower's potential stake in an investment, incentives are aligned more closely between borrower and banker thus reducing the premnium on funds. As a result, the banking sector expands. Also, to the extent that openness diversifie **s** ome risks that an economy faces regarding input or output prices, we might expect it to be less susceptible to the intemal shocks that cause large declines in borrower net worth in relatively closed economies.26 All else equal, therefore, we expect openness as measured by exports or imports as a percentage of GDP (EXP/GDP or IMP/GDP, respectively) to be positively related to growth in the financial sector.<sup>2</sup> '

**24** From the numerous cross-country studies of economic growth, one robust finding is that, holding other relevant factors constant, there is a positive correlation between the share of investment in GDP and the average share of trade in GDP. Investment share, in tum, is positively and robustly correlated with average growth rates. A possible intuition underlying this result is that openness to intemational markets enhances productivity by encouraging specialization that would be unprofitable in smaller markets. Levine and Renelt (1992), p. 953. Levine and Renelt review a number of cross-country growth regression analyses. Many including Korrnendi and Meguire (1985), Barro (1991), and Romer (1990) also find strong correlations between investment and growth. On the link between outward orientation and growth in developing countries, see Dollar ( 1992) and Pritchett (1991).

**25** Gertler and Rose (1994), p. 28.

**26** Of course, an open economy would be more susceptible to external shocks. The implicit assumption, therefore, is that the diversification opportunities afforded by openness result in shocks that are, on average, smaller than those faced by an identical, but closed economy.

**27** As Levine and Renelt are quick to point out, findings regarding EXP/GDP and investment, or EXP/GDP and growth can also be obtained almost identically using total

20
# Figure 10. Cost of inaction, new challenges

# A. Rapid debt accumulation episodes associated with financial crises

<!-- Start of picture text --> Percent of episodes<br>60<br>40<br>20<br>0<br>Government Private debt Both types of<br>debt debt<br><!-- End of picture text -->

- C. Government debt in LICs and LAC

<!-- Start of picture text --> Percent of GDP<br>150 LICs LAC<br>100<br>50<br>0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> E. Non-concessional external debt in EMDEs

# B. Outcomes of rapid government debt accumulation episodes after eight years

<!-- Start of picture text --> Index, t = 100<br>180<br>Associated with crises<br>No crises<br>160<br>140<br>*** **<br>120<br>100<br>Output Investment<br><!-- End of picture text -->

# D. Risk of external debt distress in selected countries

<!-- Start of picture text --> Percent of countries<br>Low Moderate High<br>100<br>75<br>50<br>25<br>0<br>HIPC Non-HIPC<br><!-- End of picture text --> F. Countries meeting selected DeMPA

# minimum requirements

<!-- Start of picture text --> Percent of government debt<br>90<br>85<br>80<br>75<br>2005-09 2016-20<br><!-- End of picture text --> <!-- Start of picture text --> Percent of countries<br>30<br>20<br>10<br>0<br>Monitoring of Debt reporting and<br>guarantees evaluation<br><!-- End of picture text --> 30