rafmacalaba/datause-encoder-data huggingface dataset:
of this broadening definition of trade facilitation, the definition of trade facilitation used here incorporates relatively concrete "border" elements (port efficiency and customs administration) and "inside the border" elements (domestic regulatory environment and the infrastructure to enable e-business usage).

Wilson et al. [2004] rely on three data sources - World Economic Forum Global Competitiveness Report 2001-2002 (GCR), IMD Lausanne, World Competitiveness Yearbook 2002 (WCY), and Kaufmann, Kraay and Zoido-Lobaton [2002] (KKZ). See Wilson et al. [2004] for a more complete description of the sources and each of their methodologies used to estimate the gravity model<sup>10</sup> . In this paper we summarize the main results.

Therefore:

- Port efficiency for each country is the average of two indexed inputs from GCR:

- o Port facilities and inland waterways o Air transport

- Customs environment for each country is the average of two indexed inputs from GCR:

- Hidden import barriers

- Irregular extra payments and bribes

- Regulatory environment for each country is constructed as the average of indexed inputs from WCY and KKZ:

- Transparency of government policy is satisfactory (WCY)

- Control of Corruption (KKZ)

- Service sector infrastructure for each country is from GCR: o Speed and cost of internet access

- Effect of internet on business

Figure 6 to Figure 9 report information about these indicators. The figures show the indexed inputs for regional groups of countries for each specific trade facilitation indicator<sup>11</sup> . Each indexed input is represented by a horizontal bar. The longer the bar extends to the right toward the maximum of 1.0, the higher ranked the region is in the category of trade facilitation. A vertical line is drawn at the average value. If a bar extends beyond the average for the particular trade facilitation measure, that indexed input for that region represents a condition superior to the average for all countries. For example, Figure 6 shows that OECD, Middle East and North Africa

> 10 The gravity model of international trade flows is a common approach to modeling bilateral trade flows. The standard gravity formulation includes various measures of market size (GDP, population, GDP per capita to account for intra-industry trade effects that may be associated with countries of similar incomes but varied tastes), measures of remoteness (distance and adjacency), and measures of kinship (regional trade arrangements, and language/ethnic similarities). To this basic formulation, Wilson et al.[2004] add tariffs as well as the trade facilitation indicators and some additional factors

> 11 These regional indicators use simple average of the region. An average weighted by trade or GDP would no doubt yield somewhat different results. There is no clear interpretation of alternative weighted averages. Moreover, these regional indexes are not used in estimation.

24
- 4 -

annuities, thus providing longevity risk insurance to both themselves and their spouses. A surviving spouse receives 60 per cent of the payment after the death of the main beneficiary. Retiring workers have the option of buying guaranteed annuities, which start at a lower level, but maintain payments at this level (i.e., without the 40 percent reduction) during the guaranteed period, even after their death or the death of their spouse. This type of annuity has proved very popular in Chile, as it allows more protection to the spouse and some element of bequest. The new Pension Law passed in 2004 has introduced some additional options, especially variable annuities. The new Law also introduced an innovative electronic quotation system, designed to enhance market transparency and reduce the influence of brokers in the selection of retirement products and intermediaries.

**<u>Table 1: Breakdown of Stock of Pensions, by Type of Instrument, 1985-2004</u>**

|Year|Total|P<br>Number|Ws<br>% of Total|T<br>Number|Ws<br>% of Total|Ann<br>Number|uities<br>% of Total|
|---|---|---|---|---|---|---|---|
|1985|7,609|7,373|96.8|-|0.0|236|3.2|
|1990|57,119|36,696|64.2|148|0.3|20,275|35.5|
|1995|190,400|98,699|51.8|6,803|3.6|84,898|44.6|
|2000|343,965|147,532|42.9|6,632|1.9|189,801|55.2|
|2004|520,793|196,242|37.7|6,193|1.2|318,358|61.1|

Source: SAFP Annuity providers in Chile have had access to a wide range of fixed income instruments with long durations and indexed to prices, including privately-issued instruments offering higher yields than government bonds. This access has allowed providers to hedge the complex risks associated with annuities reasonably well. Providers are also allowed to price annuities freely, and to differentiate risk according to basic annuitant characteristics, such as age, gender, and income.

The growth of the number of annuitants has led to a rapid expansion of the insurance sector, with total insurance assets growing from 5 percent of GDP in the mid-1980s to 20 percent of GDP in 2004. The large volume of pension assets – more than 60 percent of GDP – indicates that insurance assets should continue growing strongly in coming years, as these are pension accounts that will need to be converted into annuities and PWs at retirement.

The fast increase in the number of annuity contracts attracted new entrants to the life insurance market, increasing the total number of life insurance companies to 34 by the late 1990s, 23 of which were providing annuities. As shown in Figure 1 and Figure 2, the increase in the number of participants in the 1990s led to a continuous decrease in concentration ratios, quite in contrast with the pension fund sector. The access to a wide range of financial instruments allowing providers to hedge the risk of their liabilities and the high degree of competition in the annuities market has generally resulted in good outcomes for annuitants, as indicated by the high money’s worth ratios shown in the following sections.
# **Acronyms**

CLT Consolidation of the Labor Laws ( _Consolidacao das Leis Trabalhistas)_ EJA Education for Youth and Adults ( _Educação de Jovens e Adultos_ ) ENEM National High School Exam _(Exame Nacional do Ensino Médio)_ FIC Initial and Continuing Training Programs ( _Formação Inicial e Continuada_ ) FIES Financing of Higher Education Students _(Fundo de Financiamento ao Estudante do Ensino Superior)_ MTE Ministry of Labor and Employment ( _Ministério do Trabalho e Emprego)_ NGO Non-Governmental Organizations PD Positive Deviance PNAD National Sample Survey of Households ( _Pesquisa Nacional por Amostra de Domicílios)_ <mark>PRONAF National Program for the Strengthening of Family Farming</mark> ( _Programa Nacional de Fortalecimento da Agricultura Familiar)_ PRONATEC National Program for Access to Technical Education and Employment ( _Programa Nacional de Acesso ao Ensino Técnico e Emprego)_ PROUNI University for All Program _(Programa Universidade para Todos)_ TEC Long-Duration Technical Education TVET Technical Vocational Education and Training 2
(2009) database.<sup>4</sup> Reinhart and Rogoff (2009) mark a banking crisis by two types of events: (i) bank runs that lead to the closure, merging or takeover by the public sector of one or more financial institutions, and (ii) in the absence of bank runs, the closure, merging, takeover or large-scale government assistance of an important financial institution (or groups of financial institutions) that marks the start of similar outcomes for other financial institutions. This classification provides us with 42 annual crisis observations, comprising 25 systemic crisis episodes (severe financial distress, in the authors’ definition) and 17 non-systemic crises (milder financial distress) in 38 countries, which gives an in-sample frequency of crises of 3.6 percent, which is in line with similar studies (e.g. Barrell et al., 2010) and within acceptable bounds for the style of analysis.

Dating banking crises is not without drawbacks. Crises can be classified too late, because the financial problems usually begin well before a bank (or a group of banks) is finally closed or merged, or too early, because the worst of a crisis may come later. Moreover, unlike other types of financial crises such as external debt crises, which have well defined closure dates, it is difficult if not impossible to pinpoint the year in which a banking crisis ended. For these reasons, and because we are mostly concerned with investigating the effect of market concentration on the arrival of a banking crisis (i.e. the switch between non-crisis and crisis states), our binary crisis variable takes value of one if country _i_ at time _t_ enters into a crisis and zero otherwise. In other words, as in other studies (see, for example, Barrell et al., 2010), we treat crisis years other than the first (i.e. when the crisis arrives) as tranquil times. Such an approach, as well as the use of lagged control variables (see below) also allows us to control for potential endogeneity between

> 4 Available at <u>http://www.hbs.edu/faculty/initiatives/behavioral-finance-and-financial-stability/Pages/global.aspx.</u>

8
Kilby, C. 2000. Supervision and performance: the case of World. Journal of Development Economics. 62: 233‐259.

Little, I.M.D. and Mirrlees, J.A. 1991. Project Appraisal and Planning Twenty Years On. In Stanley Fischer, Dennis de Tray and Shekhar Shah, eds., Proceedings of the World Bank Annual Conference on Development Economics, 1990s. Washington, D.C. World Bank: pp.351‐82.

Little, I.M.D. and Mirrlees, J.A. 1974. Project Appraisal and Planning. London: Heinemann.

Little, I. M. D., and J. A. Mirrlees. 1969. Manual of Industrial Project Analysis. Paris: OECD Development Centre.

Squire, Lyn, and Herman G. van der Tak. 1975. Economic Analysis of Projects. Baltimore, Md.: Johns Hopkins University Press for the World Bank.

Pohl, G., D. Mihaljek. 1992. Project Evaluation and Uncertainty in Practice – A Statistical Analysis of Rate of Return Divergences of 1,015 World Bank Projects. The World Bank Economic Review. Vol. 6, No. 2:255‐ 277 Vawda, Ayesha Yaqub, Moock, J., Gittinger, J.P., and Patrinos, H. 2001. Economic Analysis of World Bank Education Projects and Project Outcomes, Policy Research Working Paper 2564, World Bank World Bank. 2010. Cost‐Benefit Analysis in World Bank Projects. Independent Evaluation Group, World Bank, Washington, DC.

World Bank. 1995. A Review of the Quality of Economic Analysis in Staff Appraisal Reports for Projects Approved in 1993. Operations Policy Department Operations Evaluation Department. May 5.

26
40 of aggregate disposable household income in 2001, the richest 10 percent earned 35 percent (World Development Indicators, 2006).

This rise in relative poverty should, of course, be seen in the context of the widening of the _overall_ dispersion of income distribution in China during the reform period; see, for instance, Renwei (2000). According to the World Bank (2003a, 2004), the Gini coefficient of per capita household income in China increased from 0.28 in 1981 to 0.32 in 1990, and to 0.43 in 2001; several other studies give similar results.<sup>70</sup> It is well known that increased income gaps across provinces and (from the mid-1980s) also between urban and rural areas have contributed to this development.<sup>71</sup> In the words of Zhang et al. (2001), China’s provinces have developed into so-called “income clubs”– with rich clubs in the eastern (coastal) regions, middle-income clubs in the center, and poor clubs in the western (highland) regions.

While the relative per capita income gap _between urban and rural areas_ fell in the early 1980s in connection with the agricultural reforms, the relative gap seems today to be approximately back at the pre-reform level, if the different price trends in urban and rural areas are properly taken into account (UNDP, 2005; Ravallion and Chen, 2006). The generally accepted assessment among observers seems to be that in 2003, per capita disposable income in urban areas was more than three times (3.3 times) higher than per capita net income (an approximation for disposable income) in rural areas<sup>72</sup> – four times higher if the provision of human services were included in the calculations (UNDP, 2005).<sup>73</sup> An important background factor is, of course, that labor productivity is about 5 times as high in manufacture and services as in agriculture (an average for industry and services).<sup>74</sup>

> 70 See UNDP (2005, Figure 2.5 and Table 2.2). Ravallion and Chen (2006, Table 10) argue that the Gini coefficient is somewhat lower after adjustment for cost of living differences between urban and rural areas: 0.40 rather than 0.45 (in 2002) without such adjustment.

> 71 For instance, Kanbur and Zhang (2005) report that the Gini coefficient for the distribution of average income _across provinces_ rose from 0.29 in 1978 to 0.37 in 2000.

> 72 Around 1990, the ratio seems to have been just above 2.

> 73 Indeed, it seems that about two-fifths of income inequality in the country as a whole in 2002 could be accounted for by the urban-rural income gap, decomposing the Theil inequality index (UNDP, 2005). 74 Kuijs and Wang (2005) assert that labor productivity is about 3.5 times as high in services as in agriculture, and about 9 times as high in industry. Holz (2005b, Fig. 1) presents even higher figures for industry (14 times higher).
# **Appendix 3: Descriptive Statistics**

<u>Table A1. Firms integrating into GVCs</u>

||Number of firms|Percentage of total firms|
|---|---|---|
|GVC1|28,681|19.97%|
|GVC2|12,485|8.69%|
|GVC3|6,083|4.24%|
|GVC4|3,288|2.29%|
|Total|50,537|35.18%|

Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

<u>Table A2. The Average Number of Female Employees, by region</u><sup>11</sup>

|Region|Female Employees|Female Production Workers|Female Non-Production Workers|
|---|---|---|---|
|SSA|14.17|14.99|5.10|
|EAP|28.49|72.15|13.85|
|ECA|24.09|27.84|8.28|
|LAC|39.77|25.59|12.98|
|MENA|11.03|17.55|5.10|
|SAR|9.33|42|4.25|

Source: Constructed by the authors using the WBES.

Note: SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA Sub-Saharan Africa, LAC Latin America and the Caribbean and ECA Europe and Central Asia.

Figure A1. The Distribution of Firms’ integration into GVC, by region <!-- Start of picture text --> 100%<br>90%<br>34.4%<br>80% 38.6%<br>43.4%<br>49.5%<br>70%<br>60%<br>22.7%<br>50% 23.2%<br>22.1%<br>40% 19.4%<br>18.8%<br>30% 13.1%<br>12.3% 8.2%<br>20%<br>8.7% 8.9% 15.8% 16.5%<br>10% 8.1% 8.1%<br>6.3% 6.1%<br>0% 5.3% 5.9% 2.0% 2.5%<br>GVC1 GVC2 GVC3 GVC4<br>SAR MENA EAP SSA LAC ECA<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Notes: (i) SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA SubSaharan Africa, LAC Latin America and the Caribbean and ECA Europe and Central Asia. (ii) GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

11 Given that some firms might have one of the variables missing, the sum of production and non-production does not necessarily add to the total number of employees.

32
<!-- Start of picture text --> Incidence of Procampo benefits<br>Poor MVulnerable Middle Class Upper Class<br>14.0% 13.3%<br>12.0%<br>10.0% 9.8%<br>10.0%<br>8.0%<br>6.0%<br>4.0% 3.6% =" 4.1%.<br>2.1% 2.3%<br>2.0% 1.3% 13% 1.8% 1.5%<br>0.0%<br>2002 2004 2006<br>Average amounts of Procampo benefits<br>BPoor MVulnerable “Middle Class Upper Class<br>$500.0 $467.3 $466.9<br>$450.0<br>$400.0 $349.0<br>$350.0<br>$300.0<br>$250.0 $214.8<br>$200.0 $188.1<br>$150.0 $1244<br>$100.0$50.0 $382 $75.7 $44.9 S847 $38.4 $69.4<br>$0.0<br>2002 2004 2006<br><!-- End of picture text -->
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36
# **3. The data set**

This study evaluates the targeting and impact of FONCODES investments using available sources of data; no new data were collected. The data for the analysis come from two main sources: (i) information on the geographic distribution of FONCODES allocations and expenditures, kept by FONCODES itself; and (ii) information from the 1994 and 1997 Peru Living Standards Measurement Survey (LSMS), and a household survey conducted by the Peruvian Statistical Institute (INEI) in 1996.

Since 1992, FONCODES has used an "allocation rule" to direct resources to small geographic jurisdictions-provinces, for 1992 through 1995, and districts since 1996. Specifically, the population of each province or district is weighted by an index of unmet tbasic needs. This index is an ad hoc composite of various measures-including access to school 'n, electricity, water, sanitation, adequate housing, and measures of chronic malnutrition and illiteracy (Schady, 1998). Provinces or districts with higher values of the FONCODES index have more unmet bas,ic needs. The index used by FONCODES has evolved over the years: until 1993, it was based on information from the 1981 population census, but it has been updatedv with information from the 1993 population census since 1994. The variables and weights in the index also changed somewhat between 1992 and 1995, but have remained constant since then. Infornation on the FONCODES index, the allocation rule, and the corresponding allocatio ris is available for 1992 through 1998.

FONCODES also keeps monthly records on the number of projects and aggregate amo-unts spent in each district. Three points are worth noting about these data. First, because expenillures are recorded in the month in which a project is _approved,_ there is a lag of about two weeks iefore _disbursement_ of the first installment of funds, and of several months before the second (anct final) disbursement. Second, only expenditures on projects are included, and not, for example, administrative costs or expenses for general overhead. Third, district-level information on f:ie allocations and expenditures is only available for the community-based projects, and not for the special projects. Finally, we also have district-level data on the expenditures made by somn other programs in the education sector.

The 1994 and 1997 LSMS and the 1996 INEI survey contain a wealth of information **on** the expenditures or income of households, education levels, and other household characteristics. The INEI survey has a large sample size-more than 18,000 households, in 403 districts. Th e

> 5 Provinces and districts correspond to the two levels of local government in Peru. In 1997, there we - 194 provinces and 1812 districts in Peru (Webb and Fernandez Baca, 1997, p. 112). The median populatimn of a district is about 4,000 people, but there is considerable variation in the number of people per districi some (rural) districts have less than 500 people, and other (urban) districts can have more than 100,000 people.

5
# **Table 3: Child diarrhea**

||(1)||(2)||(3)||(4)||(5)||
|---|---|---|---|---|---|---|---|---|---|---|
||total||rural||urban||SSA||OTHER||
|Latrine<br>_Conf. Int._|0.929<br>_(0.911, 0.949)_|***|0.919<br>_(0.898, 0.941)_|***|0.924<br>_(0.880, 0.968)_|***|0.930<br>_(0.900, 0.957)_|***|0.933<br>_(0.906, 0.960)_|***|
|Flush<br>_Conf. Int._|0.871<br>_(0.844, 0.899)_|***|0.898<br>_(0.850, 0.942)_|***|0.828<br>_(0.780, 0.872)_|***|0.843<br>_(0.792, 0.897)_|***|0.859<br>_(0.827, 0.893)_|***|
|Well/pump<br>_Conf. Int._|0.925<br>_(0.901, 0.950)_|***|0.927<br>_(0.900, 0.954)_|***|0.882<br>_(0.809, 0.963)_|***|**_0.989_**<br>**_(0.956, 1.023)_**||0.855<br>_(0.817, 0.888)_|***|
|Piped<br>_Conf. Int._|0.927<br>_(0.900, 0.956)_|***|0.927<br>_(0.896, 0.960)_|***|0.906<br>_(0.833, 0.987)_|**|0.947<br>_(0.913, 0.9969_|**|0.900<br>_(0.857, 0.935)_|***|
|Female|0.902|***|0.900|***|0.906|***|0.906|***|0.898|***|
|Age|0.977|***|0.977|***|0.976|***|0.979|***|0.975|***|
|First born|0.929|***|0.940|***|0.914|***|0.947|***|0.912|***|
|Birth interval|0.999|***|0.999|***|1.000|*|0.999|***|0.999|***|
|Vaccinated|1.238|***|1.233|***|1.259|***|1.289|***|1.182|***|
|Educ.mother|0.981|***|0.984|***|0.977|***|0.971|***|0.985|***|
|Agemother|0.959|***|0.967|***|0.947|***|0.962|***|0.956|***|
|Age squared|1.001|***|1.000|***|1.001|***|1.000|***|1.001|***|
|Mother married|0.924|***|0.946|***|0.880|***|0.942|***|0.890|***|
|Household size|1.003|***|0.999||1.010|***|1.006|***|0.998||
|Electricity|0.974|**|1.012||0.947|***|0.874|***|1.024||
|Radio|0.924|***|0.935|***|0.912|***|0.928|***|0.929|***|
|TV|0.948|***|0.971|*|0.916|***|0.941|***|0.945|***|
|Fridge|0.888|***|0.934|***|0.882|***|1.003||0.855|***|
|Bike|0.984|*|0.961|***|1.013||0.954|***|0.998||
|Urban|yes||no||no||yes||yes||
|Country|yes||yes||yes||yes||yes||
|Year|yes||yes||yes||yes||yes||
|Season|yes||yes||yes||yes||yes||
|Observations|753239||503165||250074||332115||421124||

_Notes: Logit Model. Reported coefficients are odds ratios. Year-country fixed effects. ***: p<0.01, **: p<0.05, *: p<0.10 Robust standard errors are clustered at the cluster level. For year-regional (sub-national) fixed effects estimations see Appendix A.2. Conf. Int.: 95% confidence interval. The left out category in all specifications is surface water and open defecation._ A point estimate of 0.929 for latrines in column (1) implies that basic sanitation lowers the relative probability (odds) of diarrhea by about 7 % in comparison to children without access to any sanitation technology. According to our estimates, flush toilets would lower the odds of diarrhea by about 13 % and any kind of water technology by about 7 %. For reductions in odds of around 10%, the absolute risk reductions are very similar, which makes odds ratio easier to 20/36