rafmacalaba/datause-encoder-data huggingface dataset:
7

# **II.   THE SWISS PENSION SYSTEM**

Switzerland was the first OECD country that mandated an occupational pension scheme (in 1985) as the second pillar to complement a pay-as-you-go (PAYG) system (introduced in 1948). This section provides an overview of the most salient features of the Swiss pension system with an emphasis on the old-age component. Both the first and second pillars have an embedded disability insurance, which is also briefly explained in section 2.5.<sup>6</sup>

# **2.1 The First Pillar**

# **_2.1.1 Overview_**

The first pillar, the so–called AHV<sup>7</sup> , is a pay-as-you-go (PAYG) system. It was introduced after a very successful political referendum in 1948<sup>8</sup> . It aims at providing a basic subsistence level of income to all retired residents in Switzerland.

The main features of the first pillar can be described as follows: Although there is a small trust fund, the public pension system is a pay–as–you–go system, in which the current young have to finance the pensions of the current old. The system is financed mainly with a proportional payroll tax on all labor income (see below), and an earmarked fraction of the value added tax on consumption. By law, 20 percent of total expenditures have to be financed out of general federal government revenues.

For a long time, the first pillar has been viewed as considerably stable, efficient (at reducing poverty in old age) and cheap (due to very low administration costs, approximately 0.33 percent of benefits). But as most PAYG systems in Europe, the Swiss first pillar is plagued by unfavorable demographics due to increases in longevity and low fertility rates, which have led to a strong rise in the old–age dependency ratio. If the current levels of contributions and benefits are left unchanged the present value of future contributions falls short of the present value of future claims by about a third. There are virtually no reserves — the AHV trust–fund covers less than one year’s worth of benefits (c.f. Table 1) — to cushion the anticipated population aging. Table 1 presents the revenues and expenditures of the first pillar (forecasted numbers after 2004). After 2008, the first pillar will be in deficit with an annual funding gap reaching almost 1 percent of GDP in 2015.

There is no consensus in the current political debate as to how to fix this financing problem. Swiss policy makers face strong political constraints for potential reforms. Any change of the law can be (and usually is) challenged by an optional referendum. Thus, the public possesses veto power for all reforms of the current social security system. This is

> 6 For a comparative analysis of pension systems across countries refer to OECD (2005).

> 7AHV = Alters– und Hinterbliebenen–Versicherung (old age insurance).

> 8 Changes to the law are subject to an optional referendum in Switzerland. In Switzerland, 50’000 voters (approximately 1 percent of the electorate) can request a public vote regarding law changes previously decided by the federal parliament.
Low level of capacity utilization in Mongolian firms implies that service interruptions due to inadequacies in the provision of infrastructure services did not result in large production losses.<sup>40</sup> While in East Asia each day of electrical outage results in a production decline equivalent to 0.75 percent of sales, in Mongolia the loss from each day of electrical outages (22 days a year in total) is equivalent to only 0.06 percent of sales.<sup>41</sup> Transportation services for freight movement however are limited, costly and unreliable, and are binding constraint to export growth. The majority of goods are moved by rail and rail transport costs are much higher than those in other landlocked countries in Central Asia (Table 13).<sup>42</sup> For example, while for Kazakhstan, Uzbekistan, and Kyrgyz Republic the transport costs per kilometer to transport a twenty-foot equivalent unit (TEU) container to the nearest port ($/km/TEU) are around $0.37, Mongolia’s are $0.61 – comparable to the costs faced by Tajikistan whose infrastructure was severely damaged by the civil war in the mid-nineties (1992-1997). The border crossing costs in terms of $/TEU and hours spent at the border are also much higher for Mongolia than those for landlocked countries in East Asia (e.g. Lao) and South Asia (e.g. Nepal). These costs account for as much as 28 percent of the total transit cost paid by Mongolian trading companies.<sup>43</sup>

> areas, but the number of main lines per capita is lower than that in Kyrgyzstan and Turkmenistan, and much lower than the one in China. Telephone services have become more reliable over the past 10 years, and the number of people on a waiting list for mainlines has steadily declined over the past few years. Business telephone connection charges are comparable to those in the U.S. and have declined steadily over the past 5 years.

> 40 Capacity utilization is broadly measured as the ratio of actual output to what could be potentially produced given existing human and physical resources. While capacity utilization has risen in the last three years, it is still low at just over 71 percent.

> 41 The loss from each day of water supply failure in Mongolia is 0.075 percent of sales, compared to 1.3 percent of sales in East Asia (World Bank 2006b).

42 Landlocked countries typically face much bigger constraints to trade with the outside world than coastal countries.

43 The majority of Mongolian transit traffic is carried by rail through one rail link to the nearest port of Tianjin. Because this rail operates at the wide-gauge used in Russia, rather than the standard gauge used in China, at the border either the axles on the wagons need to be changed or containers have to be transferred from one wagon to another. This operation makes up 25 percent of the overall transport cost to port.

22
_(1) n,_ = O][log n,* log n,] For estimation, we approximate this relation as (2) log n, - log nt, = O[log n,' - log n,-,] where (3) log n: _= 80 + Efij_ log _X_ **_j,_**

**_J_**

Substituting and adding a random error term we obtain the following estimating equation:

- (4) log n, - log n,, = O + E **_0j_** log **_Xj,_** Slog n,-l + ,

We fit the model to cross-country data on the growth of Internet intensity from 1990 to 1997, using the following equation and variables: (S)logNft -logN>, = _-6lognj -l_ - + logujt-l _+OAlogYj,,_ +OAlogC>1<sup>_+jk_</sup> _+ ef_ **_k_** where

- Njt = Internet subscribers/telephone mainlines

- **Ujt** = Size of urban population

- **-jt** = Income per capita

- **(it** = Index of competition policy<sup>3</sup>

- Rj = Vector of regional dummy variables (Sub-Saharan Africa, Middle East/North Africa, Asia, Latin America)

Data on Internet subscribers and telephone mainline connections have been supplied by Pyramid Research, a subsidiary of the Economist Intelligence Unit that specializes in telecom data. We have drawn measures of income per capita and urban population from World Bank sources, along with the competition policy index.

We summarize the estimation results in Table 2. Although the 44-country sample is not large, the regression statistics are quite robust. The model explains about 96% of total sample variation in the growth rate of Internet intensity. The estimated value of the diffusion parameter is almost exactly one and highly significant, suggesting little adjustment lag beyond the 7-year span of the data. Estimated elasticities for urban population and competition policy are large, positive and highly significant (.83 and .59, respectively, after accounting for the estimated value of 0 (1.04)). The competition policy result is particularly important, because policy changes can occur relatively quickly. Suppose, for example, that an African country adopts measures that improve its 3 We have used the 1995 value of this index in the regression because earlier values are not available.

4
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Table 3: Descriptive Statistics: Urban model

||**2004/05**|**2009/10**|**2011/12**|**2014/15**|
|---|---|---|---|---|
|Log HH per capita expenditure<br>Mean|4.50|4.61|4.74|.|
|HH size|||||
|1 or 2|0.07|0.08|0.08|0.09|
|3|0.10|0.11|0.12|0.13|
|4|0.22|0.24|0.24|0.25|
|5|0.21|0.20|0.20|0.20|
|District HH size|||||
|Share 1 or 2|0.06|0.07|0.08|0.08|
|Share 3|0.09|0.10|0.11|0.12|
|Share 4|0.20|0.23|0.23|0.24|
|Share 5|0.20|0.21|0.21|0.20|
|HH age structure|||||
|Share 0-14|0.31|0.29|0.27|0.26|
|Share 15-24|0.19|0.18|0.18|0.18|
|Share 25-34|0.17|0.17|0.17|0.17|
|Share 35-49|0.20|0.20|0.21|0.22|
|Share 50-64|0.10|0.11|0.11|0.12|
|District avg HH age structure|||||
|Share 0-14|0.33|0.30|0.29|0.28|
|Share 15-24|0.17|0.17|0.17|0.17|
|Share 25-34|0.16|0.17|0.17|0.17|
|Share 35-49|0.19|0.20|0.20|0.21|
|Share 50-64|0.10|0.11|0.11|0.12|
|Religion and social group|||||
|Hindu|0.78|0.78|0.77|0.76|
|Share Hindu in district|0.81|0.81|0.81|0.79|
|Share sched caste in district|0.63|0.65|0.67|0.68|
|Household type|||||
|Self-employed|0.43|0.42|0.41|0.37|
|Share self-employed in district|0.48|0.45|0.46|0.44|
|Casual laborer|0.12|0.14|0.13|0.15|
|Share casual labor in district|0.04|0.06|0.05|0.06|
|Regular wage worker|0.39|0.37|0.40|0.41|
|Share reg wage worker in distric|0.20|0.19|0.21|0.23|
|Principal industry|||||
|Agriculture|0.06|0.06|0.06|0.06|
|Industry|0.31|0.31|0.31|0.31|
|Share industry in district|0.24|0.25|0.26|0.26|
|HH expenditure|||||
|Avg misc services in district|2265|2430|2904|4015|
|Misc services|2629|2903|3427|4840|
|Avg rec services in district|247|232|345|591|
|Recreational services|328|317|433|710|
|Avg transport expenses in distri|5419|6048|8058|5905|
|Transport expenses|6817|7408|9647|6553|
|Avg transport expenses no fuel i|3007.01|2921.62|3905.65|5566.62|
|Transport expenses no fuel|3389.36|3280.83|4299.62|6137.85|
|Avg transport expenses no fuel a<br>Transport expenses no fuel and b|1101.70<br>1457.71|1092.88<br>1378.05|1640.71<br>1967.59|1964.33<br>2316.02|
|District rainfall shock|||||
|July-September|-0.20|-0.04|0.50|0.02|
|July-September (squared)|0.34|0.17|0.44|0.28|
|October-December|-0.26|0.32|-0.51|-0.07|
|October-December(squared)|0.21|0.55|0.42|0.17|
|<br>January-March|0.13|-0.25|-0.33|0.62|
|January-March (squared)|0.29|0.18|0.37|1.14|
|April-June|-0.04|-0.07|-0.18|0.42|
|April-June (squared)|0.28|0.35|0.19|0.40|

23
**Table 9.  Estimates of Tobit regression**

||**Dep. var**<br>**clust**<br>|**.:****_Qit_ = CPIA**<br>**ers A & B**<br>|**Dep. var.**<br>**Freedo**<br>|**: Economic**<br>**m Index**<br>|
|---|---|---|---|---|
|**Independent variables**|**Coeff.**|**Robust s.e.**|**Coeff.**|**Robust**<br>**s.e.**|
|∑���<br>�<br>A�<br>�.�= # policy actions raised to<br>γ= 0.0,cumulated|0.0741|0.0148***|0.9936|0.183***|
|Cumulated length of engagement|‐ 0.0025|0.0008***|‐ 0.0486|0.0130***|
|GDPper capita at PPP|0.000023|6.84e‐06***|0.0001|0.0001|
|Aid/GDP|0.5077|0.1978***|‐18.98|5.73***|
|Political Rights|‐ 0.00214|0.0222|‐ 0.204|‐ 0.302|
|Country&year fixed effects|Yes||Yes||
|N|2280||2518||
|Number of countries|145||154||

The third test of robustness is to examine whether the relationship between policy actions and the quality of government economic policy has changed over time. To do this we split the sample at breakpoints from 2000 to 2011, and estimate the main equation (8) for both the earlier sub‐period and the later. The results are reported in full in Annex C, and may be summarized as follows: using the CPIA as the dependent variable, there is a hint of a decline in the coefficient on policy actions (α) at some point 2002 and 2007, but the change is not statistically significant. Using the Economic Freedom Index as the dependent variable, there are fluctuations over time, but these are not statistically significant. Hence, overall, we are not able to conclude that there are significant changes over time in the relationship between development policy operations and the quality of economic policy.

# **7. Discussion**

We have established two ‘stylized facts’ about economic policy lending:<sup>19</sup> policy‐based operations have a positive impact on the quality of economic policy; and the number of policy actions in a given year makes no difference beyond the first. We have also found some tentative evidence that the impact of a policy engagement is strongest in the early years and, while remaining positive, tends to fall in later years.

First we consider why the number of policy actions—beyond the first—does not matter. We surmise that this is because the engagement process or “policy dialogue” is key, not so much

> 19 It is important to note that these findings are limited to economic policy actions, and may or may not be generalizable to policy‐based operations in other areas.

21
(GFLEC, 2016). Institutional reforms that accompany improved acquisition of financial skills and access to credit would be a package of interventions that would empower people, especially women. The gender gap in asset ownership is huge. On average, only 44% of women have an account at a financial institution versus nearly 70% of men. The gap is even larger for women in the bottom 40% of the income distribution. As this reform package is implemented, it is important to address gender-specific impediments in the financial sector, including biases in inheritance and family laws which limit the collateral available to women. We rate this constraint as severe from a gender perspective and give it a score of 4 in column (5) of Table 1. Finally, since most poor women are stuck in small, mixed cropping agriculture, any intervention addressing this constraint will have to consider the distortions preventing productive employment for women in agriculture (either as workers or as farm owners). We therefore rate this constraint as severe from a gender inclusion perspective and give it a rating of 4 in column (5) of Table 1.

The remaining binding constraints to growth in column (2) of Table 1 are unlikely to interact negatively with existing gender related distortions. Therefore, they receive a lower rating of 2 as shown in column (5) of Table 1. Note however that the constraints related to access to basic services (education) and resources (land and water), and regional differences receive a higher rating of 3 in terms of gender inclusion because interventions targeting these areas are likely to reduce gender gaps. The sum of columns (2) and (5) allows us to obtain the new ranking from the gender-enhanced growth diagnostics. The most binding constraints to gender-enhanced growth are those with a rating of 8 in column 6 of Table 1:

- underdeveloped financial markets;

- low female labor force participation;

- low performance in technology absorption and innovation.

Identifying the policies that would effectively address gender gaps within each of these areas requires careful impact evaluations, which are beyond the scope of this paper. For further information on this topic we refer readers to papers by Buvinic (2015, 2018), Bandiera et al. (various years), and Goldstein et al. (various years), among many others. Nevertheless, our aim is to show that even a simple yet systematic approach to identifying dual binding constraints has policy merit since it narrows priority areas and makes implementation of policies to address them more likely.

# **5. Concluding Remarks and Caveats**

The value of the gender-enhanced diagnostic approach is that it offers the chance to engage with governments around a common theme of increasing the productivity of the economy and driving new sources of economic growth based on scalable policy interventions that empower women economically. National governments are much more likely to see merit in a broad set of policy-relevant interventions that favor women’s economic empowerment, if in addition to helping countries achieve Sustainable 15
impact-damage ratio and impact multiplier is rather larger, and even the largest. These imply again that meteorological disasters wipe out a large extent of areas and a large range of activities resulting in greater total impacts than other types of disasters. In fact, out of the top ten events with largest impact multipliers, seven events are meteorological ones (in Madagascar and Guatemala), two are hydrological (in Madagascar and Bangladesh), and one is geophysical (in Guatemala).

<!-- Start of picture text --> 160,000<br>140,000<br>120,000<br>100,000<br>80,000<br>60,000<br>40,000<br>20,000<br>0<br>Damages Estimated Losses Estimated Total Impacts<br>196019611962196319641965196619671968196919701971197219731974197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007<br><!-- End of picture text --> Figure 4-1. Historical Trends of Economic Impacts<sup>20</sup> The historical trends of economic impacts for the 184 cases mimic the one in figure 2-2 based on EM-DAT data. Figure 4-1 illustrates the trends of aggregated damages, losses, and total impacts for each year. A gradual increase of all the three economic impacts is observed until year 2000, with an exception of 1995 including the Hanshin-Awaji (Kobe) Earthquake in Japan. Between 2001 and 2004, a lull of economic impacts was observed; and then, year 2005 becomes another exception with Hurricane Katrina in the U.S. This lull is not found in figure 2-2; the difference between figures 2-2 and 4-1 results from the screening of events, in which the cases in figure 4-1 are selected with some certain size in damages (grater than US$ 20 million; larger than 1% of GDP for developed countries or 2% of GDP for developed countries). For instance, multi-country disasters, such as the 2004 Indian Ocean Earthquake and

> 20 Values are in constant 2007 US$ million.  Lines in the figure are the linear regression line for each item, and all three lines are statistically significant with 5% level (please see the details in Appendix 2).

16
the right answer may depend on the context.<sup>40</sup> Informative questions with one correct answer were difficult to find, precisely because the difficulty to make the right decisions in farming often comes from the difficulty to adapt to each context rather than applying a “one size fits all” solution. Obtaining better measures of technical skills may require the development of new techniques that assess whether the numerous micro-decisions taken by a farmer fit his environment.

# _5._ **CONCLUSIONS**

Cognitive, noncognitive and technical skills are thought to play a key role for many economic decisions and outcomes in developing countries and are increasingly incorporated in empirical analyses. Little is known, however, about the validity or reliability of commonly used skill measures in surveys conducted in developing countries. This study is the first to investigate the reliability, validity, and the predictive power of a large range of skill measures on a poor rural adult population in a developing country setting. We do so using data from a survey experiment, specifically designed for this purpose, and a variety of statistical tools and methodologies. The results show the cognitive skills measures are reliable and internally consistent, while technical skills are difficult to capture and very noisy. The evidence further suggests that measurement error in noncognitive skills is non-classical, as correlation between questions are driven in part by the answering patterns of the respondents and the phrasing of the questions.

These results first of all suggest that collecting comparable information on cognitive skills in large household surveys in field conditions is feasible. Further validation of such measures in other contexts will be important to establish whether these conclusions hold in different settings – including possibly when cognitive measures are less correlated to educational achievement – and the extent to which such measures allow to compare cognitive outcomes across countries, or across regions/groups within a country.

The study further shows how specifically accounting for measurement error through factor analysis and item response theory can help increase the validity, reliability and predictive power of the technical skill measures. It also highlights that obtaining a good aggregate and stable measure of agricultural knowledge is challenging, as the “right” answer to many agricultural

> 40 For instance, the optimal number of seeds in a hole at planting can depend on the quality of the seeds and the spacing between seeds, and when farmers answer this question, their benchmark quality and spacing might be different than those of the agronomist. And their answers may change over time if answers reflect their most recent experiences.

31
variables in the participation equation have the expected signs: the likelihood of participating in the labor market increases with age, education level, and if the individual is married.<sup>7</sup> The negative sign of the lambda coefficient means that there is a negative correlation between unobserved characteristics that affect labor force participation and wage earnings. Individuals who are less likely to participate in the labor market, but that effectively work, will earn lower wages.

Table 6 - Percentage of Formal vs. Informal Labor by Level of Education

|Education|199|8|200|6|
|---|---|---|---|---|
||**Informal**|**Formal**|**Informal**|**Formal**|
|Illiterate|94%|6%|90%|10%|
|Read & Write|82%|18%|79%|21%|
|Primary|90%|10%|80%|20%|
|Preparatory|91%|9%|74%|26%|
|General Secondary|68%|32%|69%|31%|
|Voc. Sec. Agriculture|63%|37%|68%|32%|
|Voc. Sec. Industrial|65%|35%|65%|35%|
|Voc. Sec. Commerce & Others|59%|41%|47%|53%|
|Vocational Sec. (5-Yr)|54%|46%|3%|97%|
|Post-secondary|27%|73%|32%|68%|
|University (4-Yrs)|31%|69%|27%|73%|
|University (5-Yrs)|22%|78%|18%|82%|
|Post-graduate|20%|80%|14%|86%|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

The estimated coefficients on education levels from the earnings equation (Table 7) allow computing the rates of return as described in equation 2. Figure 1 summarizes the results. There are two general trends that are remarkable: (a) the rate of return increases with the level of education; and (b) the rate of return increased for higher levels of education, but decreased for lower levels of education between 1998 and 2006. Although in 1998 the RoRs have a U-shaped form, given the very high rate of return to

> 7 This result is driven by male characteristics and will change when gender heterogeneity is allowed for in the next section.

9