rafmacalaba/datause-encoder-data huggingface dataset:
49

## **Table 6 (continued)**

## **Variable Definitions**

Note: All variables are derived from the PENDAT file described in Zorn (1991) unless otherwise indicated. All variables are qualitative (0,1) unless speci;ied.

## **Dependent Variables**

|YRIROR|Annual rate of retmun reported in 1991 survey (%).|
|---|---|
|YR5ROR:|Average annualized rate of return 1986-90 reported in 1991 survey (%).|
|RETURNSD:|Return Variability (%) = (([1990 Return- (1986-90 Average Return)J*2)*l/2)<br>/<br>(1986-90 Average Retum)|
|AST_PBO:|Ratio of reported pension system assets to PBO rr.asure of cumulative plan<br>liabilities (%).|
|FLOWFUND:|Ratio or annual actual to required employer plan contributions (%).|

# **Independent Variables**

## _A. Pension Board Composition_

|BDELAC:|Fraction of pension Board elected by active employees (%).|
|---|---|
|BDELRT:|Fraction of pension Board elected by retired employees (%).|
|BDBENOK:|Board is required to authorize benefit amounts.|
|BDACTOK:|Board is required to authorize acruarial assumptions.|
|LIABINS:|Board is covered by liability insurance.|
|_B. Pension Man_<br>ADINVST:|_agement Practices_<br>Administrative cost charged to pension investment income.|
|INVINHS:|Investment staff of pension portfolio is partly (or fully) managed in-house.|
|ACCINHS:|Accounting staff needs of pension system partly (or fully) met in-house.|
|TOPIOMG:|Plan investments are handled partly (or fully) by external money managers in the|

Olivia S. Mitchell - Public Pension Governance and Performance
# **E.20 Bounding Exercise: Unintended Increase**

Data on Ebola incidence in Sierra Leone is incomplete. As such, we cannot directly rule out an increase in exposure by comparing the total number of cases in treatment and control areas. To be a confirmed case in the available Ebola data, an individual must be infected with Ebola and known to health workers through self-reporting or surveillance. CM and NFA could theoretically affect case counts by unintentionally increasing either exposure rates, reporting propensities, or both. We use our empirical results and a simple model to clarify what must be assumed to attribute our results to changes in exposure.

## **Sequence and Information**

Each individual _i_ observes whether they are symptomatic, _s ∈{_ 0 _,_ 1 _}_ . The CDC lists the following as Ebola symptoms: fever, severe headache, muscle pain, weakness or fatigue, diarrhea, vomiting abdominal pain, or unexplained hemorrhage. They also observe the treatment status of their local health facility, _T ∈{_ 0 _,_ 1 _}_ .

_i_ knows that Pr[ _s_ = 1 _| I_ = 1] = 1: if you have Ebola, you will show symptoms. They also know that Pr[ _s_ = 1 _| I_ = 0] = _p ∈_ (0 _,_ 1), i.e., that symptoms like fevers and diarrhea happen to those that are not infected.<sup>A7</sup> The infection rates within control and treatment communities, _eT_ = E( _I | T_ ), are also common knowledge.

_i_ must to decide whether to report their symptoms and be tested, _R ∈{_ 0 _,_ 1 _}_ . They cannot, however, condition this decision on their actual infection status, because this is not known to _i_ prior to testing.

## **Notation**

- Reporting among Symptomatic in Control: Pr( _R | s_ = 1 _, T_ = 0) = _h ∈_ [0 _,_ 1]

- Reporting among Symptomatic in Treatment: Pr( _R | s_ = 1 _, T_ = 1) = min _{hτh,_ 1 _}_ where _τh ∈_ R<sup>1</sup> +

- Reporting among Asymptomatic in Control: Pr( _R | s_ = 0 _, T_ = 0) = _l ∈_ [0 _,_ 1]

- Reporting among Asymptomatic in Treatment: Pr( _R | s_ = 0 _, T_ = 1) = min _{lτl,_ 1 _}_ where _τl ∈_ R<sup>1</sup> +

We assume _l ≤ h_ (i.e., individuals with symptoms are more likely to report than those without). To minimize terms, we define _d_ = _l/h_ . This is the ratio of reporting probabilities of asymptomatic to symptomatic individuals in control areas. _d_ = 0 _._ 5, for example, implies that symptomatic individuals in control areas are twice as likely to report as those displaying no symptoms.

> A7This is likely a considerable proportion of Sierra Leone’s population: the WHO, for example, cites a 2011 assessment which found that 24 percent of children under 5 had malaria in the _two weeks_ prior to the survey. Over months, the probability of flu-like symptoms due to illnesses unrelated to Ebola is quite likely.

A58
As in the case of the Caribbean as a whole, the intra-regional traffic among the countries covered has been declining precipitously, contracting annually at a rate of 4.5 percent. In contrast, domestic connectivity seems to be growing significantly, if admittedly from a low base level (Figure 8b). This is a very interesting dynamic as in relative terms domestic traffic is nowadays more significant than regional traffic by more than a full percentage point (Figure 8a). To put things in perspective, there are 117 routes combining all routes originated by covered countries (domestic and international intra-Caribbean). These routes carry about 4.8 million passenger-seats, of which 60 percent of the routes and 63 percent of the traffic is internal to each covered country (as opposed to between covered countries).

**Figure 8: CGF Air Traffic Capacity by Type of Route**

<!-- Start of picture text --> a. Composition in million seats, % of total (2013) b. Annual Growth Rates (%)<br>6.0% 5.0%<br>3.0, 9.7%<br>4.0% 3.0%<br>2.7, 8.5% 2.1% 2.1%<br>1.7%<br>2.0% 1.2%<br>0.0%<br>-2.0%<br>25.7,<br>81.9%<br>-4.0% -3.0%<br>-4.5%<br>-6.0%<br>Domestic CGF Extra-regional<br>Growth 2001-13 Growth 2008-13<br>Annual Growth Rate<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ Traffic is also highly concentrated along country types. The so-called hub-countries – The Bahamas, Barbados, the Dominican Republic, Jamaica, and Trinidad and Tobago— capture 82 percent of the total air movement and their traffic grows at a solid 2.5 percent every year. Over 40 percent of that traffic is linked to the Dominican Republic, concentrating one third of the total air movement (Table 7). The share of The Bahamas and particularly of Barbados seems to be receding.

In a far second, the group of small island states –essentially the OECS countries— reaches an estimated seat capacity of 2.5 million per year (7.8 percent of the total traffic capacity), with Antigua and St. Lucia pulling together two thirds of the total OECS (small island states) traffic. Without exception, the air transport capacity of every single country in the small island states has declined. The most salient cases are St. Vincent and St. Kitts, whose traffic declined by an annual rate of 14 and 9 percent respectively over the period 2008-13 (see Table 7).

31
16 be able to offset the likely contractionary effects of an improvement in saving.

According to an econometric model of Mexico's manufacturing trade balance (cf. Annex 2), the largest share of the recent deterioration of Mexico's manufacturing trade balance was accounted for by the appreciation of Mexico's real exchange rate (obtained comparing Mexican and US wages, cf. Figure 7). According to our simulations, had Mexican dollar wages increased at the rate of US inflation since the end of 1988 (i.e., had the real exchange rate remained constant), Mexico's manufacturing trade balance (including maquila) would have been 58e smaller than it actually was in 1991. **15** One argument often invoked in favor of Mexico's external competitiveness is that the real exchange rate based on Mexican wages is substantially more depreciated than what the exchange rate based on the CPI or PPI indices suggest. It is indeed true that real exchange based on Mexican wages (and the Us CPI index) is substantially more depreciated than the real exchange rate based on the CPI price index (46% between 1980 and mid-1992 compared to 7.41 according to a trade weighted CPI-based exchange rate). However, a measure of the real exchange rate based on wages adjusted for gross labor productivity in manufacturing, shows that in 1990 the peso was only 20.4% more depreciated than in 1980 16; this is only slightly more than the 7.4% real depreciation implied by the CPI-based real exchange rate.

According to our simulations, other cyclical factors of the

**15** At the time this econometric analysis was done Cmid-1992), only quorterLy data to 1991 was avaiLable. The period covered is 1980-91.

**16** Note that reaL wages in 1950 were at historicalty very high leveLs and the reaL exchange rate that year is often viewed as if it had been overvaLued (at least for the expansionist policies of that time). Mexican labor productivity growth picked up sharply in the earty 1990s, but so did average reat wages.
Figure 11: Stricter employment protection legislation is associated with a higher share of temporary workers in the OECD <!-- Start of picture text --> CHL<br>POL<br>ESP NLD<br>KOR<br>PRT<br>SWE<br>ISL<br>CHE FIN FRA<br>CAN JPN SVNLUX DEU<br>NOR<br>ITA<br>IRL<br>DNK AUT<br>BEL<br>GRC<br>GBR AUS CZE<br>SVK<br>EST<br>coef = 4.1404662, (robust) se = 1.7671858, t = 2.34<br>-1 -.5 0 .5 1<br>Strictness of employment protection legislation<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> Source: OECD Employment and Labour Market Statistics database, 2014 edition; World Bank, World Development Indicators database, 2014 edition.

Note: The strictness of employment protection legislation is measured using the OECD index on the strictness of regulations on individual and collective dismissals of permanent employees. The index is a synthetic indicator of the strictness of procedures and costs involved in dismissing individuals or groups of workers on regular contracts, with higher values indicating stricter regulations. Data are for 2013. The relationship is significant at the 5 percent level after controlling for GDP per capita.

The incidence of temporary contracts varies across age groups and is particularly high among youth (ages 15–24). In Sweden 55 percent of employed youth have a temporary contract, more than twice the share in Denmark (21 percent) and more than three times the share in the United Kingdom (15 percent). The share of employed youth with a temporary contract in Sweden is also higher than the already high average in the EU15 countries (43 percent) and twice the share in OECD countries (25 percent) (Figure 12).

The large share of young workers with a temporary contract raises concerns about social cohesion, because half of young workers face very different labor market conditions than their pair. Workers with temporary contracts tend to have “second-best jobs” in terms of both employment conditions and job stability, and to be paid less. When there is asymmetry of legislation between temporary and permanent contracts, temporary workers tend to have a higher risk of unemployment, unclear career 25
8 the reproductive age.<sup>19</sup> The conflict between productive and reproductive decisions is obvious. In fact, it is this a,-ymmetry, in part biological and in pdrt. stemming from societal norms, which largely destines women to the observed employment and pay characteristics in the labor market.

In conclusion, the country studies confirm that women's decision to participate in the labor market depends, on the one hand, on education and, on the other hand, on their demographic characteristics. Of course, these results are partly based on the assumpticn. that these characteristics are exogenous to the participatior functions, and this assumption is not necessarily appropriate. However, the magnitude and consistency of results are sufficiently clear for the limited generalizations attempted ldter in this paper.

19 See chapter on "Female Labor Force Participation in Latin America: Patterns and Trends, _1950-_ 1985" in Psacharopoulos and Tzannatos (1991).
Table 1: Probability of having a safe job based on marginal effects of the multinomial logit

||Model 1:|Model 2:|Model 3:||Model 5:|Model 6:|Model 7:|
|---|---|---|---|---|---|---|---|
||Raw gaps|Raw gaps|Individual|Model 4:|Language|Barriers to|Networks|
|VARIABLES||by origin|characteristics|NUTS2 FE|ability|certification||
|Foreign born|-0.101***<br>(0.004)|||||||
|Origin: EU15||0.040***|0.013|-0.006|0.034***|0.033**|0.028*|
|||(0.009)|(0.009)|(0.009)|(0.095)|(0.010)|(0.016)|
|Origin: NMS13||-0.189***|-0.168***|-0.162***|-0.095***|-0.089***|-0.051**|
|||(0.007)|(0.008)|(0.009)|(0.012)|(0.012)|(0.019)|
|Origin: EFTA||-0.022|-0.032|-0.009|0.003|0.002|-0.023|
|||(0.026)|(0.028)|(0.028)|(0.028)|(0.028)|(0.058)|
|Origin: other Europe||-0.207***|-0.196***|-0.192***|-0.135***|-0.128***|-0.110***|
|||(0.008)|(0.008)|(0.008)|(0.011)|(0.012)|(0.018)|
|Origin: MENA||-0.086***|-0.068***|-0.085***|-0.027|-0.017|0.022|
|||(0.015)|(0.017)|(0.017)|(0.019)|(0.019)|(0.029)|
|Origin: Sub-Saharan Africa||-0.089***|-0.084***|-0.104***|-0.060***|-0.057***|-0.039|
|||(0.014)|(0.015)|(0.015)|(0.016)|(0.016)|(0.025)|
|Origin: Asia||-0.126***|-0.121***|-0.134***|-0.066***|-0.062***|-0.033|
|||(0.010)|(0.011)|(0.011)|(0.014)|(0.014)|(0.022)|
|Origin: LAC||-0.183***|-0.172***|-0.131***|-0.105***|-0.092***|-0.061**|
|||(0.011)|(0.012)|(0.013)|(0.014)|(0.014)|(0.022)|
|Origin: non-Europe OECD||0.215***|0.151***|0.130***|0.146***|0.150***|0.163***|
|||(0.020)|(0.023)|(0.023)|(0.023)|(0.023)|(0.033)|
|Age|||<br>0.005***|<br>0.008***|<br>0.008***|<br>0.008***|<br>0.006***|
||||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age^2|||0.000***|-0.000***|-0.000***|-0.000***|-0.000***|
||||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Male|||0.032***|0.034***|0.034***|0.034***|0.034***|
||||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Years of Education|||0.053***|0.054***|0.054***|0.054***|0.055***|
||||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Language – advance|||||-0.065***|-0.060***|-0.058***|
||||||(0.011)|(0.011)|(0.016)|
|Language – intermediate|||||<br>-0.114***<br>|<br>-0.103***<br>|<br>-0.091***<br>|
||||||(0.013)|(0.013)|(0.019)|
|Language – beginner|||||-0.093***<br>(0.018)|-0.086***<br>(0.018)|-0.079***<br>(0.024)|
|Barriers Certification||||||-0.161***|-0.171***|
|||||||(0.021)|(0.028)|
|Job through networks*Migrant|||||||<br>-0.060***<br>(0.014)|
|Job through networks*Native|||||||<br>-0.063***<br>|
||||||||(0.005)|
|Observations|395,284|395,284|394,291|394,291|393,300|393,300|<br>373,350|
|R-squared|0.003|0.006|0.078|0.088|0.089|0.089|0.088|
|NUTS2 division FE|No|No|No|Yes|Yes|Yes|Yes|
|P(safe jobs)|0.452|0.451|0.460|0.465|0.465|0.465|0.472|

Note: Robust standard errors are reported in parentheses; ***: p<0.01, **: p<0.05, *: p<0.1.

43
Policy Research Working Paper 6451

# **Abstract**

This paper assembles data at the all-India level and for the village of Palanpur, Uttar Pradesh, to document the growing importance, and influence, of the non-farm sector in the rural economy between the early 1980s and late 2000s. The suggestion from the combined National Sample Survey and Palanpur data is of a slow process of non-farm diversification, whose distributional incidence, on the margin, is increasingly pro-poor. The village-level analysis documents that the non-farm sector is not only increasing incomes and reducing poverty, but appears as well to be breaking down long-standing barriers to mobility among the poorest segments of rural society. Efforts by the government of India to accelerate the process of diversification could thus yield significant returns in terms of declining poverty and increased income mobility. The evidence from Palanpur also shows, however, that at the village-level a significant increase in income inequality has accompanied diversification away from the farm. A growing literature argues that such a rise in inequality could affect the fabric of village society, the way in which village institutions function and evolve, and the scope for collective action at the village level. Failure to keep such inequalities in check could thus undermine the pro-poor impacts from the process of structural transformation currently underway in rural India.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at planjouw@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4. Concentration and quality search: The evidence**

Quality screening of suppliers can be expected to be more important for products whose quality matters and where it is not standardized across suppliers. We attempt here to identify this type of effect by exploring how product type correlates with the concentration of supply sources using Rauch’s classification of goods into homogenous, reference-priced, and differentiated. Rauch’s classification has the advantage of being, in principle, orthogonal to economies of scale, as it characterizes goods rather than their production process (although the two may be related). Regression results are shown in Table 8.

Table 8 Regression results, concentration and product differentiation

||Herfindahl<br>(1)|Theil<br>(2)|# partners<br>(3)|
|---|---|---|---|
|Time|-0.006|-0.011|-0.002|
||(19.17)***|(16.36)***|(0.15)|
|Time^2|0.000|0.000|0.004|
||(5.52)***|(1.30)|(17.22)***|
|Reference price|-0.023|0.013|0.188|
||(10.58)***|(2.85)***|(2.39)**|
|Differentiated|-0.138<br>(64.69)***|-0.204<br> (45.50)***|5.151<br> (66.65)***|
|Importer GDP|0.000|0.000|0.000|
||(5.12)***|(2.60)***|(13.49)***|
|Constant|0.898<br>(97.80)***|4.742<br> (245.33)***|-1.357<br>(4.07)***|
|Observations|99'638|99'638|99'638|
|R-squared|0.15|0.19|0.21|
|Importer FE|yes|yes|yes|

Compared to homogenous goods (the omitted category), differentiated goods are characterized by significantly lower geographical concentration, although the effect is quantitatively small.

The model of section 2 also suggested that, for products subject to supplier screening, diversification should take place by “bouts”, followed by re28
# **Table 3c.  Trade Flows of Uganda**

|||Imports|||Exports||
|---|---|---|---|---|---|---|
||USD in|||USD in|||
||<br>billion|% of total|% of supply|<br>billion|% of total|% of output|
|**Business Services**|||||||
|Air Transport|0.1|3.2|69.4|0.0|0.1|6.0|
|Communication|0.0|0.6|5.5|0.0|1.1|12.3|
|Insurance|0.0|1.8|36.9|0.0|0.5|16.2|
|Business Services nec|0.1|3.7|13.4|0.0|0.6|2.7|
|Financial Services nec|0.0|0.4|2.0|0.0|0.4|2.4|
|Transport nec|0.0|1.3|7.8|0.1|4.6|31.6|
|Water Transport|0.0|0.1|40.8|0.0|0.0|20.4|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|0.7|23.8|45.7|0.3|8.8|28.8|
|Energy and Minerals|0.0|0.9|14.2|1.2|40.3|90.5|
|Food Products|0.2|6.9|8.5|0.3|11.1|14.6|
|Petroleum and Coal Products|0.3|11.0|64.9|0.0|0.2|4.7|
|Other Manufacturing|0.8|30.4|62.8|0.0|1.4|9.8|
|Textile and Apparel|0.1|4.9|40.4|0.0|1.1|16.8|
|Wood and Paper Products|0.1|3.7|31.5|0.0|0.5|6.6|
|**CRTS**|||||||
|Agriculture and Forestry|0.1|2.6|2.8|0.6|18.9|18.6|
|Other Services|0.1|2.1|1.1|0.2|6.6|3.8|
|Trade|0.1|2.0|3.0|0.1|2.6|4.3|
|Utilities|0.0|0.6|3.0|0.0|1.4|8.2|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

49
Figure 35. Debit Card Ownership across Education Levels <!-- Start of picture text --> 2011 2014<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Turkey BRIC Rest of High Income Rest of ECA Turkey BRIC Rest of High Rest of ECA<br>Developing Developing Income<br>World World<br>Primary Secondary Tertiary<br><!-- End of picture text --> _<u>Source</u>_ : FINDEX 2011 and 2014 Figure 36. Debit Card Ownership across Education Levels <!-- Start of picture text --> 2011 2014<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Turkey BRIC Rest of High Income Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Developing<br>World World<br>Primary Secondary Tertiary<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 41
grouping of banks to ensure that comparisons are between like entities (see Barltrop and McNaughton [1992], specially Section 2 in Volume 2). The inter-comparison of banks may be too ambitious given the practical difficulties involved, specially if one wants to incorporate the macro-economy. A more realistic approach may be to compute "standardized" risk and performance indicators that then may be used to assess the long-term health of an individual bank or group of banks.

At an aggregated level, our financial module will coincide with the Monetary Survey. When available, we propose to use the more comprehensive Banking Survey, which includes Other Banking Institutions (see IFS). Throughout the paper we will distinguish three main financial agencies or subsectors: The Central Bank, the Analysis Banks, and Other Financial Institutions. The balance sheet of the Central Bank follows closely the presentation in the IFS. rhe Analysis Banks include the set of banks, or particular bank, that we want to exarnine in depth. The asset-liability structure of the Analysis Banks is the minimum needed for the computation of "core" performance and risk indicators. That is, we want to introduce enough detail in the balance sheet to allow for the computation of the main indicators usually computed by financial analysts. Finally, the data for Other Financial Institutions will be obtained residually, so that the three sub-sectors are fully compatible with the Monetary Survey.

4
Table 4 1931-37 Percentage Growth of Output for a Cross Section of Countries

||Dependent|Variable|
|---|---|---|
|Explanatory<br>Yariabl|Industrial<br>Producdon|GDP|
|Constant|0.353|0.151|
||(0.094)|(0.056)|
|Percentage change in<br>dependent variable<br>1929-31|-0.417<br>(0.419)|-1.313<br>(0.403)|
|% of total government|0.002|0.002|
|<br>debt in default|(0.001)|(0.001)|
|Percentage change in|-0.074|-0.089|
|terms of trade<br>1931-37|(0.023)|(0.174)|
|Standard error of<br>regression|0.258|.188|
|Rs2|.113|.522|
|Number of observations|25|24|

Notes: Standard errors in parentheses.

Source: See text.

23