rafmacalaba/datause-encoder-data huggingface dataset:
_PENSIONREFORMINLATiNAMERICa_

**_Page 11_**

of the high commissions. As assets accumulate and operating costs moderate, net returns will likely increase.

||**Size of Funds**<br>|||
|---|---|---|---|
||Level<br>bn pesos<br>|Level<br>% GDP<br>|Change<br>% GDP|
|December 1994<br>|0.52<br>|0.18<br>||
|March 1995<br>|0.95|0.35|0.17|
|June 1995<br>|1.36<br>|0.47|0.12|
|September 1995<br>|1.89<br>|0.70<br>|0.23|
|December 1995<br>|2.50|0.91|0.21|
|March 1996<br>|3.22|1.22|0.31|
|June 1996<br>|3.84|1.40|0.18|
|September 1996<br>|4.49<br>|1.63<br>|0.23<br>|
|December 1996<br>|5.33<br>|1.91|0.28|
|March 1997|6.24|2.20|0.29|

_Source:_ **Dinniri** Vittas, "The Argentine Pension Reform and its Relevance for Eastern Europe" pp. 22 The Argentine system shares similarities as well as differences with the Chilean system. Argentina has less extensive pension privatization and the new system provides a basic pension to most workers who have the opportunity to remain with the unfunded public pillar. The costs of the pension system are higher in Argentina because of the higher level of pensions and a greater degree of redistribution. Both countries systems are characterized by a gap between the number of affiliates and active contributors.

# D. **Mexico**

Since 1944, Mexico has had a social security system for formal private sector workers and the self-employed. It consisted not only of retirement benefits but also retiree health benefits and disability. Approximately 80 percent of the formal labor force is covered. Contributions are 8.5 percent of wages. Other pension plans cover public sector workers and the military. Self-employed agricultural workers and up to 10 million underemployed and unemployed workers are not covered by the system. The present system guarantees a minimum pension equal to the minimum wage. At least 10 years of contributions are necessary for eligibility for the minimum pension.

Reform of Mexico's social security system was undertaken because of the financial fragility of the system. Increased life expectancy and lower population growth have led to a rapid aging of the population -- notwithstanding a large share of young people in the population. These demographic pressures compounded the more immediate severe financial disequilibrium. At inception, pension surpluses were designed to finance the structural requirements of the social security system and to fund health and maternity insurance. In fact, pension reserves did not accumulate until 1960, 16 years after the system was established. Meanwhile, benefits outpaced contributions. Benefits were originally designed to cover only the retired worker and not dependents. Moreover, before 1989 minimum pensions increased from 35 percent of the minimum wage to 100 percent in 1995. The latter steps was taken owing to the sharp decline in the real value of pensions. In 1989 pensions were indexed to the minimum wage. The financial status of the social security system in 1996 suggested the need for either a three-fold increase in contributions or an increase in government contributions totaling 1 percent of GDP in the year 2000 and 3.75 percent of GDP in 2030.
11 the Soviet era, “taxpayers” were “large in size and small in number”, and the state had many other levers of control besides taxes (Martinez-Vazquez & Wallace, 1999). However, in a shift from a centrally controlled to a market economy, the fiscal system needed to be reformed. These changes represented significant shocks to ordinary citizens. In particular, for the first time, individuals were faced with the direct payment of taxes, including being asked to file different tax returns. The transition process brought up many policy questions, among others: the tax system, the structure of tax administration, and the degree of political participation. Voluntary compliance and self-filing, two important pillars in a modern tax system, were completely absent just after the planned socialism (Martinez-Vazquez & McNab, 2000). Following the early transition, tax evasion and avoidance reached very high levels, as the new tax administration was not prepared to enforce taxes in a market-based economy with large numbers of taxpayers. Tax administration was poorly equipped (e.g., inappropriate use of modern computers and office technology) and managed. Information was used ineffectively, the collection enforcement was weak and there existed substantial corruption practices (Trasberg, 2005). Further, the connection between the payment of taxes and the provision of public goods had been largely concealed under socialism, which might have reduced the identification with the state and thus the willingness to pay taxes.

Profeta (2005) stresses: “Radical reform means that not only do the new fiscal institutions have to introduce new instruments of taxation, a transparent fiscal system, and a well-working tax administration, but also that they have to correct attitudes, incentives and relations” (p. 65). Easter (2007), e.g., reports the findings of a poll conducted by the tax administration in the Tambov region in the early years of the transition, which indicated that only one-third of the respondents argued that paying taxes to the state would provide any benefit to themselves.

In the transition process, revenue needs are an important issue. Gordon (1994) points out that in the reform process, governments in Eastern Europe are confronted with
participate in the experiment, we abstract from liquidity constraints in our estimations of demand for these products.

One month after the original experimental session, a series of input fairs were held in each of the villages were sessions had been held. All participants were invited to the fair and, once at the fair, they were given the option of purchasing inputs. Participants in treatments 3 and 4 were provided with the remaining money that had been in savings with the group treasurer, and any interest that was due was paid. Participants in treatment 4 (savings for emergencies) were also offered the opportunity to save again with the group treasurer for further safe keeping over a three-month period, and at the same interest rate as they had been offered earlier (T4+). These interest payment were made in October, at the same time that insurance payouts were also due. Because of favorable weather conditions that year, no insurance payouts were made. Figure 1 below summarizes the project timeline.

**Figure 1: Project Timeline (2013)**

|June|Experimental sessions<br>Financial products offer|Baseline Survey|
|---|---|---|
|July|Input fair<br>Interest payment on agricultural investment and emergency<br>savings products (T3 and T4)|Midline survey|
|October|Insurance term<br>Interest payment on extended agricultural savings product (T4+)||
|December||Endline survey|

# **3.** **<u>Empirical approach</u>**

The random allocation of participants into these four treatment groups allows us to examine the welfare impact of each of these products by comparing the behavioral changes across groups. The provision of an endowment to each individual to spend on a product ensured that take-up was high across all products affording us with some power with which to assess differences in outcomes. The fact that the same endowment was offered across all groups to all individuals, allows us to estimate the differential impact of the type of financial product offered.

To increase power we also run LATE estimation models with take-up instrumented with the interest rate on savings, the price of insurance and the day on which the experimental session took place (this was also randomized and we expect subjective expectations about the probability distribution of yields to change as more information about the season becomes available over time).

In our analysis we specifically examine the following questions:

- **_The effectiveness of insurance versus targeted savings in encouraging productive investment and improving welfare:_** We compare agricultural investments between participants in T1 to those

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**Table 3. Extensive margin of cross-border financial investments**

|**Level of Data Aggregation**||**Country-Co**|**untry Level**||
|---|---|---|---|---|
|**Dependent Variable**|**Indicator V**<br>|**ariable: =1 if inv**<br>|**estment>0 and**|**=0 otherwise**|
|**Database**|**Portfolio**<br>**Holdings**|**Syndicated**<br>**Loans**|**M&A**|**Greenfield**|
|North-North Trend|0.118 ***|0.029 ***|0.031 ***|0.026 ***|
|North-South Trend|0.078 ***|0.008 ***|0.042 ***|0.038 ***|
|North-LAC Trend|0.052 ***|-0.015 **|0.025 ***|0.062 ***|
|South-North Trend|0.113 ***|0.047 ***|0.049 ***|0.050 ***|
|South-South Trend|0.148 ***|0.051 ***|0.062 ***|0.057 ***|
|South-LAC Trend|0.107 ***|0.040 ***|0.070 ***|0.084 ***|
|LAC-North Trend|0.127 ***|0.055 ***|0.046 ***|0.028|
|LAC-South Trend|0.097 ***|0.063 ***|0.046 ***|0.047 ***|
|LAC-LAC Trend|0.085 ***|0.058 ***|0.045 ***|0.067 ***|
|**Gravity Controls**|||||
|Geographical Distance between Countries|-0.614 ***|-0.671 ***|-0.704 ***|-0.794 ***|
|Differences in Latitude|0.000|0.003 ***|0.001|-0.002 ***|
|Differences in Longitude|0.004 ***|0.001|0.004 ***|0.002 **|
|Differences in Time Zones|-0.039 **|0.019|-0.033 *|0.022|
|Colony Dummy|0.336 ***|0.511 ***|0.482 ***|0.483 ***|
|Common Legal Origin Dummy|0.207 ***|0.078 **|0.239 ***|0.145 ***|
|Common Language Dummy|0.420 ***|0.419 ***|0.354 ***|0.479 ***|
|Region-pair Dummies|Yes|Yes|Yes|Yes|
|Source Country Dummies|Yes|Yes|Yes|Yes|
|Receiver Country Dummies|Yes|Yes|Yes|Yes|
|**Number of Observations**|120,078|264,401|386,584|217,350|
|**Differences between Trend Coefficients**|||||
|(LAC-North) Trend - (South-North) Trend|0.014|0.008|-0.002|-0.021|
|(LAC-South) Trend - (South-South) Trend|-0.051 ***|0.013|-0.016|-0.009|
|(LAC-LAC) Trend - (LAC-South) Trend|-0.012|-0.005|-0.002|0.019|
|(North-LAC) Trend - (North-South) Trend|-0.025 ***|-0.022 ***|-0.017 ***|0.024 **|
|(South-LAC)Trend -(South-South)Trend|-0.042 ***|-0.011|0.008|0.027 **|

This table shows the different trends for the extensive margin. The regressions are Probit models where the dependent variable is an indicator variable that takes the value of 1 whenever there is a bilateral positive flow (stock) between the two countries involved, and 0 otherwise. The level of data aggregation is country-country level. The regressions include gravity control variables which help explain different levels of financial flows between each country pair based on the geographic distance between the two countries (in logs), differences in latitude and longitude, differences in times zones, whether the receiver (sender) country is (or has been) a colony of the sender (receiver), whether they have a common legal origin, and whether they share a common language. The regressions also control for source and receiver country dummies and region pair dummies. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Standard errors are cluster by country-pairs. *, **, and *** represent statistical significance at 10%, 5%, and 1%, respectively.
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# **Table 15-5. Infrastructure and services (cont’d)**

|Percent||Nair|obi|||Dak|ar||
|---|---|---|---|---|---|---|---|---|
||Po|or|Non|poor|Poo|r|Non|poor|
|Indicator|N|Value|N|Value|N|Value|N|Value|
|_Health services_|||||||||
|% of 0–14-year-olds immunized against BCG (tuberculosis)<sup>1</sup>|1,559|97.8|207|96.6|5,509|99.0|875|98.3|
|% of households who say health services exist in their settlements_b, f_|1,282|87.4|473|92.3|1,615|89.6|345|85.5|
|_School facilities_|||||||||
|% of HHs who say public school services exist in their settlements_f_|1,282|50.9|473|54.4|1,615|59.7|345|57.6|
|% of HHs who say private school services exist in their settlements_a_|1,282|84.0|473|88.4|1,615|57.9|345|59.3|
|_Road facilities_|||||||||
|% of HHs who say internal road systems exist within their settlements_f_|1,282|97.4|473|98.0|1,615|36.2|345|29.0|
|% of HHs who say access road services to their settlements exist c|1,282|98.0|473|98.9|1,615|54.5|345|60.2|
|_Primary mode of transportation_to school and work by individuals (%)|||||||||
|By foot|2,259|58.1|421|42.9|5,111|39.1|838|42.2|
|Bike|47|1.2|17|1.7|29|0.2|7|0.4|
|Private car|1|0.0|5|0.5|188|1.4|52|2.6|
|Shared taxi / Gypsy taxi|3|0.1|0|0.0|84|0.6|16|0.8|
|Taxi|0|0.0|2|0.2|20|0.2|16|0.8|
|Microbus/matatu|595|15.3|367|37.4|80|0.6|9|0.5|
|Bus regular|13|0.3|3|0.3|1,921|14.7|309|15.6|
|Other|40|1.0|18|1.8|227|1.7|34|1.7|
|NA|896|23.0|137|14.0|4,933|37.7|0|0.0|
|No response/ Do not Know|36|0.9|11|1.1|480|3.8|705|35.5|
|TOTAL|3,890|100|981|100|13,073|100|1,986|100|
|_Selective overall infrastructure access_|||||||||
|% of households who has neither piped water or electricity a|1,282|68.6|473|59.1|1,615|2.7|345|2.9|
|% of households who has both piped water and electricity a|1,282|4.8|473|10.9|1,615|74.9|345|79.5|
|% of households who has no piped water or electricity or permanent wall a|1,282|65.2|473|56.3|1,615|0.3|345|1.1|
|% of households who has piped water and electricity and permanent wall a|1,282|2.0|473|7.1|1,615|73.7|345|76.9|

_Note:_ Statistical significance for difference in the corresponding indicator value between “poor” and “nonpoor” households: (1) In Nairobi: “a”— 1% level; “b”—5% level; “c”—10% level; (2) In Dakar: “d”—1% level; “e”—5% level; “f”—10% level.

1 Based on individual level survey data that is not weighted since the available sampling weight information is at the household unit level.

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**Fig. 1.** Sensors and other technologies in a future AV

(source: Center for Sustainable Systems, 2018).

# **2.2 Plausible AMS operation scenarios**

Since AVs in an AMS do not rely on human drivers, future mobility would look quite differently from what it is today. While research on future transportation with AVs has been booming, a consensus has not been achieved on the exact operation scenario of future transportation systems. Three plausible scenarios have attracted most attention:

- _Autonomous mobility with private ownership:_ In this scenario, most vehicles running on the street will be driverless and owned by individual households. The possibility of household members coordinating schedules so that a single AV can accommodate their daily activities has been investigated. Relevant research has been further extended to looking into AVs coownership by residents in a neighborhood or a community, which can reduce the ownership cost of each individual and increase use flexibility.

- _Shared autonomous mobility:_ The second scenario also conceives a world dominated by AVs. However, these AVs will be assets of large fleet operators such as a driverless version of future transportation network companies such as Uber and Lyft. Given that all major auto manufacturers nowadays are partnering with AV technology companies and/or developing AVs independently, it is possible that auto manufacturers may also become AV mobility service providers.

- _A mixed private and shared autonomous mobility with coexistence of human-driven vehicles:_ This may be the most plausible scenario considering the long transition time from today to a complete self-driving environment, which may take at least a few decades (Fig. 2). A mix of traffic will persist on the road, including privately owned human-driven vehicles, shared human-driven vehicles, privately owned AVs, and shared AVs (see Fig. 3, which forecasts the total miles driven in the United States). The coexistence of the four types of vehicles would make traffic control very complex and  challenging for modeling and analysis (Noruzoliaee and Zou, 2019).

Note that most transportation systems of today have car and non-car modes. The coexistence of different modes is also expected to persist in the foreseeable future. Public transportation is an important non-car mode in helping mitigate traffic congestion, energy use, and emissions. In a future AMS, alternative modes other than AVs, especially those that could be immune to cyber attacks, would 4
**Table 12: Composition analysis – Conn’s (2014) sample of pedagogical studies**

||**Glewwe et al.**<br>**2014**|**Kremer, Brannen,**<br>**& Glennerster**<br>**2013**|**Krishnaratne, White,**<br>**& Carpenter 2013**|**McEwan 2014**|**Murnane & Ganimian**<br>**2014**|
|---|---|---|---|---|---|
|Abdu-Raheem (2012) - Nigeria|-|-|-|-|-|
|Ajaja and Eravwoke (2010) - Nigeria|-|-|-|-|-|
|Bimbola and Daniel (2010) - Nigeria|-|-|-|-|-|
|Brooker et al. (2013) - Kenya|-|-|Health intervention|-|-|
|Githau and Nyabwa (2008) - Kenya|-|-|-|-|-|
|Kiboss (2012) - Kenya|-|-|-|-|-|
|Korsah et al. (2010) - Ghana|-|-|-|-|-|
|Louw, Muller, and Tredoux (2008) -<br>South Africa|_Missing code_|-|-|-|-|
|Lucas et al. (forthcoming) - Kenya and<br>Uganda|-|-|-|Instructional<br>materials/<br>Teacher training|Scripted lessons|
|Lucas and Mbiti (2012) - Kenya|-|-|-|-|Fee abolition|
|Nwagbo (2006) - Nigeria|-|-|-|-|-|
|Piper and Korda (2011) - Liberia|-|-|-|-|Feedback for teachers|
|Piper (2009) - South Africa|-|-|-|-|-|
|Sailors et al. (2010) - South Africa|-|-|-|-|-|
|Spratt, King, and Bulat (2013) - Mali|-|-|-|-|-|
|Van Staden (2011) - South Africa|-|-|-|-|-|
|Wachanga and Mwangi (2004) -<br>Kenya|-|-|-|-|-|
|Percentage of studies included in<br>review|6%|0%|6%|6%|18%|
|Percentage of RCT studies included in<br>review|0%|0%|6%|6%|12%|

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# **<u>Table 5. Propensity shares for "Other Oceania" split for Portugal</u>**

|**_Country_**|**_Propensity share_**|**_Share*No._**<br>**_migrants to be_**<br>**_assigned_**<br>**_(rounded)_**|
|---|---|---|
|Australia|0.25863|2|
|New Zealand|0.36202|2|
|American Samoa|0.02795|0|
|Cook Islands|0.01569|0|
|Fiji|0.09991|1|
|French Polynesia|0.00064|0|
|Guam|0.05765|0|
|Kiribati|0.00170|0|
|Marshall Islands|0.00606|0|
|Federated States of Micronesia|0.01331|0|
|Nauru|0.00066|0|
|New Caledonia|0.00112|0|
|Norfolk Island|0.00026|0|
|Northern Marianas|0.00518|0|
|Niue|0.00458|0|
|Palau|0.00390|0|
|Papua New Guinea|0.02229|0|
|Samoa|0.07457|1|
|Solomon Islands|0.00188|0|
|Tokelau|0.00170|0|
|Tonga|0.03565|0|
|<br>Tuvalu|0.00109|0|
|Vanuatu|0.00166|0|
|Wallis andFortuna|0.00189|0|
|**Total**|1|6|

# **_Version 4c_**

Some countries provide no bilateral information, and so we have only the totals supplied by the United Nations. As the main aim of the project was to produce a full bilateral matrix, it was important to calculate these coefficients. These were calculated on the same basis as the remainder, "Other" categories, i.e. based on the equation in 4b above, the propensity for countries to send people abroad, with the only difference being that rather than restricting the number of countries used to calculate the shares to those countries comprising the remainder category, the shares were calculated for all 226 countries, i.e. global as opposed to regional shares. The 30 countries until now lacking bilateral data therefore had all their coefficients estimated. Lastly, once the foreign-born matrix was filled, all of the data prior to 2000 were scaled to the United Nations (2004) mid-year-totals for 2000 so that a complete bilateral matrix for the foreign-born for the years 2000-2002 resulted (data for 2001 and 2002 were not scaled to the United Nations totals). Although a later United Nations dataset could have been utilized to extrapolate the data further into the future, it was considered more prudent in terms of accuracy to use that year for which most of the data are recorded. This allowed the inclusion of many countries that have neither collected the relevant migration statistics nor released them. The drawback in these circumstances is that the migration history between the date that the older source was conducted and 2000/01 will be lost as the relative shares are maintained over time. Table 6 provides details of the data contained in each version, together with the number of immigrants included.

14
Another problem with the view that the global market will be swamped by products from China and India is that the law of comparative advantage entails that there will always be opportunities for other countries to export even though India and China will come to dominate certain sectors. In general, as the global demand for say Chinese manufactured products increases, (dollar denominated) wages in China will tend to increase, through higher wage demands from Chinese workers (especially if the rural and urban labor markets remain partially segmented) and from the inevitable additional pressure on the yuan to rise. There is evidence that this process has already begun (Figure 5).

<!-- Start of picture text --> Figure 5: China’s surge – and that of other developing countries –<br>represents more opportunity than threat<br>� Exporting requires importing<br>� Surging growth means rising wages<br>Internationally comparable average wage rates, indexed, 1998=100<br>230<br>C h in a<br>180<br>M e x ic o<br>130 S o u th A fric a<br>In d ia<br>80<br>P h ilip p in e s<br>30<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Based on scenarios from World Bank Economic Prospects 2007<br><!-- End of picture text --> Thus, Chinese development will not preclude the lowest income countries being able to export low skilled intensive products. However, this will only arise if these countries put in place a business climate that supports investment and trade. For example, in Africa, competitiveness from low cost labor is undermined by high indirect costs, with the main barriers being corruption, crime and inadequate infrastructure (Eifert et al 2005). The poor business environment leads to lower returns to labor and capital in production which depresses investment, labor demand and real wages.

The entry of these large economic entities into the global market offers opportunities as enormous as the challenges posed for developing countries. The large markets in India and China have changed the dynamic of south-south trade (Figure 6) and offer developing countries a route to decreased dependence on rich countries, demand in which for products produced in the poorest countries has been growing slowly for a long period.

10