rafmacalaba/datause-encoder-data huggingface dataset:
To analyze the implications of alternative economic and political scenarios for pension financing in Germany, the public pension system commissioned a study from the Swiss consulting firm _Prognos AG._ The first study was conducted in 1987.It concluded that the basic structure of the German pension system could be maintained provided that changes were made in the key parameters of the system, such as the retirement age, the benefit formula and the participation of the government in financing. After reunification, which placed a heavy burden on the pension insurance and after the pension reform of 1992, a second study was commissioned from the same firm to determine whether the current structure was still feasible under the new conditions.

The study uses two scenarios for the projections which are run first in a macro model and then in the pensions model for the period of 1992-2040. Varying assumptions are made with respect to internationalization of trade and capital flows, technological change and innovation, wage growth and productivity increases and migration to Germany. In both scenarios, the population is shrinking despite migration leading to a decline in the labor force.

**Table 4: Projected pension contribution rates** (in percent of wages)

|______________________Optimistic|||Scenario|Pessimisti|c Scenario|
|---|---|---|---|---|---|
||_1992|2010|2040|2010|2040|
|Pension contribution|19.2|22.4|26.38.7|||
|Total payroll tax|41.5|43.4|48.6|44.5|52.7|

Source: Prognos AG, 1995 As shown in table 4, pension contribution rates are expected to increase from 19.2 percent today to 26.3 percent in 2040 in the optimistic scenario and to 28.7 percent in the pessimistic scenario. Without the measures of the 1992 pension reform the projected increase of the contribution rate would be twice as high.Total payroll taxes for social insurance, i.e. pensions, health, long term care and unemployment insurance, will grow from 41.5 percent today to 48.6 and 52.7 percent, respectively. This result seems surprising given the expected dramatic increase of the system dependency ratio.

The three reasons for the relatively moderate increase of the contribution rates are the long term evolution of the demographic dependency ratio, the employment situation and well as the measures taken during the 1992 pension reform, particularly the automatic adjustment of the federal subsidy and the adjustment of pensions to net wage increases.The demographic dependency ratio increases in both scenarios to 66.8 and 72.6 percent, respectively, and then falls by 3 about percentage points between 2030 and 2040; these numbers take into account the resident population in Germany.

17
**_Table 1: SAR Lagging Behind All but SSA in Access to Infrastructure Services_**

||Avg GDP<br>Growth<br>(2000-2012)|Urbanization<br>Rate (2012)|Telecom<br>Access (per<br>100 people)<br>(2011)<sup>1</sup>|Electricity<br>Access (%<br>of pop.)<br>(2010)<sup>2</sup>|Access to<br>Improved<br>Sanitation (% of<br>pop.) (2011)<sup>3</sup>|Access to<br>Improved<br>Water (% of<br>pop.) (2011)<sup>4</sup>|
|---|---|---|---|---|---|---|
|EAP|8.9|50|98|92|67|91|
|ECA|4.4|60|157|100|94|95|
|LAC|3.1|79|125|94|81|94|
|MNA|4.2|60|105|94|89|89|
|SAR|6.7|31|72|71|39|90|
|SSA|4.7|37|54|35|30|63|
|World|2.5|53|103|78|64|89|

_Source:_ World Development Indicators, except when noted otherwise. _Notes:_ 1. Telecom access is defined as the number of fixed and mobile lines; 2. World Energy Outlook 2010 by International Energy Association; 3. Improved sanitation is defined as connection to a public sewer, a septic system, pour-flush latrine, simple pit latrine, and ventilated improved pit latrine; 4. Improved water is defined as household connection, public standpipe, borehole, protected dug well, protected spring, rainwater collection.

# **3.2 Heterogeneity in Infrastructure Coverage among SAR Countries**

**Sri Lanka and Maldives have the best access rates in the region.** More than 90 percent of their population has access to improved sanitation, which is better than in LAC at 81 percent. In terms of electrification, only Maldives (95 percent) and Sri Lanka (77 percent) are above the average rate for developing countries (76 percent).<sup>8,9</sup> On telecom, Sri Lanka and Maldives top the lists with 104 and 173 telephone lines per 100 people. This places Sri Lanka almost at the world average of 103 lines per 100 people and above EAP (98 lines per 100 people).

**Afghanistan, Nepal, and Bangladesh have the worst access rates in the region.** Nepal, with the lowest number of telephone lines per 100 people in SAR (47), is behind Afghanistan (54) – which matches SSA (54). For electrification, Afghanistan, not surprisingly, is the worst; a meager 30 percent of the population can rely on electricity powered lighting at night. Moreover, Afghanistan and Bangladesh (47 percent) are closer to the 35 percent found in SSA than to the 71 percent found in SAR. Total road network (km) per 1,000 people is also low in Nepal, Afghanistan, and Bangladesh—in Maldives it is also low, but it is explained by geographical reasons. And only 29 percent of Afghanistan’s roads, and 10 percent of Bangladesh’s roads, are paved.

**The exception is high average access to improved water in SAR, and not just in a few countries.** Five of the eight countries in SAR (i.e., Bhutan, India, Maldives, Pakistan and Sri Lanka) have access rates to improved water of at least 90 percent, similar to the 94 percent rate found in LAC.

> 8 It should be noted that data sources are kept the same for consistency purposes when comparing countries. The Ceylon Electricity Board (CEB) estimates for example that over 90 percent of Sri Lankan households were electrified in 2011. 9 World Energy Outlook, IEA:

> http://www.worldenergyoutlook.org/resources/energydevelopment/globalstatusofmodernenergyaccess/

7
**Table 5. Objective vs. Subjective Indicators**

|||<br>**Probit Estimates**|**Poor access to**<br>**- Marginal effects**||
|---|---|---|---|---|
||**Labor regulation is**<br>**an obstacle**|**Power shortage is**<br>**an obstacle**|**finance is as an**<br>**obstacle**|**Corruption is an**<br>**obstacle**|
|**Number of labor inspections**|0.052*<br>(0.012)||||
|**Business age**|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|**Log employment**|0.002|0.022*|0.007|-0.006|
||(0.007)|(0.008)|(0.007)|(0.007)|
|**Located in a big city**|0.009|-0.081**|-0.006|0.043|
||(0.031)|(0.034)|(0.033)|(0.029)|
|**% sales lost because of power outages**||0.009*<br>(0.001)|||
|**Share of external finance**|||-0.001*<br>(0.000)||
|**Pays bribes**||||0.156*<br>(0.017)|
|**Dummies for Industry**|yes|yes|yes|yes|
|**Dummies for States**|yes|yes|yes|yes|
|**Dummies for Year**|yes|yes|yes|yes|
|**Observations**|2,061|3,100|2,469|3,603|
|**R-squared**<br>**note: .01 - ***; .05 - **; .1 - *;**|0.084|0.139|0.065|0.050|

(Standard Errors in parentheses) 22
**Table (1)** Average Respondent, Household, and Shelter Characteristics

|||Treatment|Control|Difference|(se)|N|
|---|---|---|---|---|---|---|
|**Panel**|**A: Respondent characteristics**||||||
|(1)|Female respondent (=1)|0.46|0.52|-0.05**|(0.03)|1,619|
|(2)|Age (categorical)|33.99|34.19|-0.21|(0.61)|1,619|
|(3)|Married (=1)|0.84|0.84|0.00|(0.02)|1,616|
|(4)|Disabled (Washington Group, =1)|0.26|0.25|0.02|(0.03)|1,616|
|**Panel**|**B: Household characteristics**||||||
|(5)|Dependency Ratio|1.31|1.27|0.04|(0.07)|1,616|
|(6)|Household size|5.20|5.14|0.06|(0.15)|1,616|
|(7)|Number of families in the same house|1.30|1.30|-0.00|(0.04)|1,616|
|(8)|Number of children|2.96|2.89|0.07|(0.12)|1,619|
|**Panel**|**C: Shelter characteristics**||||||
|(9)|Access to piped water (=1)|0.66|0.67|-0.01|(0.07)|1,616|
|(10)|Fully constructed roof (=1)|0.12|0.08|0.04**|(0.02)|1,616|
|(11)|Functional windows (=1)|0.22|0.22|-0.01|(0.03)|1,616|
|(12)|Completed foor (=1)|0.44|0.40|0.04|(0.06)|1,616|
|(13)|Toilet (=1)|0.93|0.92|0.01|(0.04)|1,616|
|(14)|Plan to stay in shelter (=1)|0.92|0.92|0.00|(0.02)|1,537|
|(15)|Monthly rent (USD PPP)|328.02|335.28|-7.26|(17.57)|1,431|
|(16)|Lease contract (=1)|0.77|0.78|-0.01|(0.04)|1,431|
|(17)|Number of times moved shelter|0.47|0.44|0.03|(0.08)|1,616|
|(18)|Permanent shelter (=1)<br>_Joint signifcance p-val:_ 0.102|0.91|0.86|0.05|(0.06)|1,616|

_Notes_ : This table reports treatment balance results for the 1,619 household interviewed at midline from the 2,017 households sampled for the study (see Table 3). We have admin data from the implementing partner’s integrated assessment but only for the 1,619 households that we were able to reach at midline. The first two columns report averages by group, while the third column reports results of estimating _yi_ = _β_ 0 + _β_ 1 _Ti_ + _εi_ . Robust standard errors in parentheses in the forth column and are clustered at the locality level. The last column reports the total sample size. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All outcomes are taken from the implementing organization’s data, except for gender and age, which are taken from the phone survey. “USD” denotes United States Dollars and “PPP” stands for Purchasing Power Parity, which is used to compare the absolute purchasing power of countries’ currencies. Rent is winsorized at the top 1% of values, in order to limit the influence of outliers. Disability is calculated using the Washington Group Short Set on Disability and uses the recommended cut-off (level 3) to classify respondents as being disabled or not based on six domains: seeing, hearing, walking, cognition, self-care, and communication. For household size, we drop one observation in which the number of individuals at baseline is reported to be 300. For times changed shelter, we drop one observation in which the respondent is reported to have changed shelter 48 times.

40
_-_ 22 _-_ <!-- Start of picture text --> f \ ~~~~~~~NE<br>,NB a sUnion's reaetion curve<br>Government's reaction curve<br>\ 0s ~~~~~~~~~~e<br>'0<br>Union<br>L Government<br><!-- End of picture text --> Tho lnteruectlon between the reactLon curves ln the upper panel is the Nash equLlibrLum. SubeLdLes are on the horLzontal and wages on the vertical axis . In the lower panel the curves show the optlmal relatLonshLp between employment and the subsidy parameter for, respectLvely, the union (eq. (3.15)) and the government (eq. (3.17)). The LntersectLon between these curves represents **_the_** subsLdy parameter and employment for the Nash equilibrium. An increase of i will lead to a horizontal shLft of the government's reactLon curve to the left (and likewLie for the curve for eq. (3.17)) and hence lead to lower employment.

The Stackelberg equilibriu, with the government as the leader We now assum that the government sets Lts subeldy parameter before the WCP sets the wage and employment. The government acts as a Stackelberg leader maximizing its utllity takLng into account the fLrm's reaction to Lts polLcy _s0_ if 1/8 e S /(l='a)(1/8) subsLdy parameter 5%, employment L and wages W4 are saLmlar Ln thLe Stackelberg equillbrLum to the solutLons for the Nash equlILbrLum. ThLi can be grasped from Flgure 3.2. The Stackelborg '2 Calculatlons for both Stackelberg settLngs are avaLlable on request from the authors.
resources from coffee and cocoa producers to government coffers (Varangis, 1989). Additional taxes on exports were levied to fund public sector activities that were supposed to support the coffee and cocoa sectors. With exchange rates better aligned in recent years (Schiff, 2001), and the elimination of export taxes, the coffee and cocoa exports should, ceteris paribus<sup>3</sup> , be more profitable than previously.

It should be pointed out that in recent months there has been a rapid devaluation of the Dominican peso, largely as a result of severe problems in the financial sector (related to a scandal at the commercial bank Baninter). The Dominican peso was about DR$17/US$ in July 2002 and about RDS27/US$ in the end of May 2003. This rapid devaluation (the Dominican Peso lost about 60% of its value relative to the US$ from January to May 2003) is causing economic instability and increasing the rate of inflation.

# **B. Population, Per Capita GDP and Poverty**

Economic restructuring and the emergence of tourism and light manufacturing industries have been associated with increased urbanization, changes in employment patterns and impacts on the distribution of economic gains. Some of these changes are described below.

# **B.1 Population**

Population increased from 5.2 million in 1980 to 8.4 million in 1999 (see table I.4), while population growth rates fell from 2.2% from 1980-1990 to 1.8% per annum from 1990-1999. Over time, a major shift has taken place in urban-rural population compositions. The rural population has remained stable at about 3 million people, while urban populations almost doubled from 2.9 to 5.4 million from 1980 to 1999. Consequently the share of rural population decreased from 49% in 1980 to 36% in 1999. Most of this change is due to increased rural-urban migration and not to differences in fertility.

|**Table I.4: Do**|**minican Republic: Pop**|**ulation and GDP per Ca**|**pita 1970– 1999**|
|---|---|---|---|
||Population<br>In Millions|Per Capita GDP<br>RD$in Real Prices|Per Capita GDP<br>US$ Current Prices|
|1970|4.0|371|325|
|1975|4.7|487|655|
|1980|5.4|544|988|
|1985|6.1|535|834|
|1990|6.8|548|813|
|1995|7.6|601|1597|
|1999|8.4|740|2082|

Source: ECLAC (2001) Population Data: US Census Bureau website.

# **B.2 Per Capita GDP and Poverty**

From 1970 to 1999 per capita GDP in constant RD$ doubled (see table I.4). Although some improvements in poverty and employment have occurred, persistent poverty, especially in rural areas, and high inequality have accompanied the high overall economic growth rates of the 1990s. For example, in 1998, the wealthiest 20% of the population accounted for 47% of total income

> 3 The term “ceteris paribus” is key, because other conditions have changed that affect the profitability of coffee and cocoa, notably international market prices.

11
simultaneously with changes in misallocation. Although a good case can be made that the repeal of ulcra came as a surprise, matters may be less clear for changes in stamp duty taxation. The second worry is that these policies are particular. We expect them to affect mostly the property markets. It is unclear whether our findings for these two policies can be generalized to policies affecting other factors of production.

With this in mind, we now turn to a broader set of policies affecting labor markets, land markets, entry into manufacturing, foreign investment, and international trade.

The regulation of the land and labor markets in India is made at the state level. There is a lot of cross-sectional variation between states with, perhaps at one extreme, a state like Gujarat where the outlook is pro-market, and at the other, West Bengal, which was historically ruled by communists-led coalitions. There have also been many changes over the years. These changes were recorded by Besley and Burgess (2000) for the land market and Besley and Burgess (2004) for the labor markets. These data series were subsequently expanded to 1997 by Aghion _et al._ (2008).

The of labor market and is for our coding changes by Besley Burgess straightforward purpose. Any change in labor market regulations in a state for any given year is assessed to be either or We to create more pro-worker pro-employer. expect pro-worker changes frictions on the labor market and increase misallocation. We expect pro-employer changes to work in the opposite direction.

Matters are more complex for the land reforms. First, in large part they concerned rural land since Besley and Burgess (2000) find much larger effects of these reforms in rural areas than in urban areas. These reforms have affected misallocation in may manufacturing through the location choices of manufacturing establishments and may be part of the explanation behind the decentralisation of manufacturing in India (Ghani, Goswami, and Kerr, 2012). Land reforms are also more heterogeneous and their effects on the functioning of land markets less obvious. A first group of reforms concerned the regulation of tenancy. These reforms attempted to give clearer rights to tenants. While greater rights for tenants can lead landowners to hold on to parcels that are inefficiently 28
Sánchez-Martín, Escribano and de Arce, 2014.

“How regional integration and transnational energy networks have boosted FDI in Turkey” Tagliapietra, S., 2013. "Towards a New Eastern Mediterranean Energy Corridor? Natural Gas Developments Between Market Opportunities and Geopolitical Risks". Working Papers 2013.12, Fondazione Eni Enrico Mattei.

Tatoglu, E. and Glaister, K., 1998. “Western MNCs’ FDI in Turkey: an analysis of location specific factors”. _Management International Review_ , 38(20), 133–59.

Tatoglu, E. and Glaiser, K., 2000. _Dimensions of Western Foreign Direct Investment in Turkey_ (Westport, CT: Quorum Books).

Tatoglu, E., Glaister, K. and Erdal, F., 2003. "Determinants of Foreign Ownership in Turkish Manufacturing," _Eastern European Economics_ , M.E. Sharpe, Inc., vol. 41(2), pages 5-41, March.

Te Velde, D. and Bezemer, D., 2006. “Regional Integration and Foreign Direct Investment in Developing Countries”. In _Transnational Corporations_ , Vol. 15, No. 2. UNCTAD. Available at <u>http://www.unctad.org/en/docs/iteiit20062a3_en.pdf</u> Tulug, S., 2004. “What Drives Foreign Direct Investment into Emerging Markets? Evidence from Turkey” _Emerging Markets Finance & Trade_ , Vol. 40, No. 4 (Jul. Aug., 2004), pp. 101-114 Uckun, D. and Doerr, M., 2010. "Emerging Markets: Theory & Practice. Turkey’s Reforms Post 2001 Crisis," _Journal of Global Analysis_ , Centre for Strategic Research and Analysis, vol. 1(1), pages 51-74, January.

Uctum, M. and Uctum, R., 2005. "Portfolio Flows, Foreign Direct Investment, Crises," _Computing in Economics and Finance_ 2005 224, Society for Computational Economics.

Utkulu, U. and Seymen, D., 2004 “Revealed Comparative Advantage and Competitiveness: Evidence for Turkey vis-à-vis the EU/15”, DokuzEylulUniversity – Economics Department, Izmir, September 2004.

World Bank, 2014. _<u>Evaluation of the EU-Turkey Customs Union</u>_ <u>. World Bank Report No. 85830-TR.</u> YASED, 2013. “Barometer Survey of Perceptions and Expectations of Foreign investors”. Uluslararası Yatırımcılar Derneği. February 14<sup>th</sup> , 2013.

Zaman, M., 2005. "Assessing The Impact Of Turkish Negotiations For Entry Into The European Union On Fdi And Trade In The Region" (May 2005). International Trade and Finance Association 15th International Conference. Working Paper 47. http://services.bepress.com/itfa/15th/art47 33
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15
# **Table 7: Conley Bounds**

||Endogenous|variable: travel c|ost|
|---|---|---|---|
|Dependent variable:|IV: N|aturalpath||
||δ:[-0.0001,0.0001]|-0.14831738|-0.11838858|
||δ:[-0.001,0.001]|-0.14927312|-0.11743313|
|wealth index|δ:[-0.01,0.01]|-0.15883091|-0.10787822|
||Endogenous varia<br>|ble: conflict near<br>|markets<br>|
||IV: Fractionalization n<br>fractionalization ne<br>borde|ear market, squ<br>ar market, distan<br>r from market|ared term of<br>ce to east|
||δ:[-0.0001,0.0001]|-0.12900793|-0.09872961|
||δ:[-0.001,0.001]|-0.12919092|-0.0985378|
|wealth index|δ:[-0.01,0.01]|-0.13102262|-0.09661798|
||Endogenous variabl|e: conflict near h|ouseholds|
||IV: Fractionalization n<br>of fractionalization n<br>east borde|ear households,<br>ear households,<br>r from househol|squared term<br>distance to<br>ds|
||δ:[-0.0001,0.0001]|-0.26683725|-0.19892408|
||δ:[-0.001,0.001]|-0.26779058|-0.19806024|
|wealth index|δ:[-0.01,0.01]|-0.27734444|-0.18940086|
|||Lower Bound|Upper Bound|
||Endogenous|variable: travel c|ost|
|Dependent variable:|IV: N|aturalpath||
||δ:[-0.0001,0.0001]|0.0256589|0.04427896|
||δ:[-0.001,0.001]|0.02470355|0.04523484|
|Multi-dimensionalpovertyindicator|δ:[-0.01,0.01]|0.01514969|0.0547939|
||Endogenous varia|ble: conflict near|markets|
||IV: Fractionalization n<br>fractionalization ne<br>borde|ear market, squ<br>ar market, distan<br>r from market|ared term of<br>ce to east|
||δ:[-0.0001,0.0001]|0.01648337|0.03508131|
||δ:[-0.001,0.001]|0.01629361|0.03526633|
|Multi-dimensionalpovertyindicator|δ:[-0.01,0.01]|0.01439252|0.03711992|
||Endogenous variabl|e: conflict near h|ouseholds|
||IV: Fractionalization n<br>of fractionalization n<br>east borde|ear households,<br>ear households,<br>r from househol|squared term<br>distance to<br>ds|
||δ:[-0.0001,0.0001]|0.01922971|0.05855597|
||δ:[-0.001,0.001]|0.01832708|0.05947112|
|Multi-dimensionalpovertyindicator|δ:[-0.01,0.01]|0.00926055|0.0686626|

39