rafmacalaba/datause-encoder-data huggingface dataset:
13 The cooperative Nash-bargained model **assumoes** the partners cooperatively maximize a product of the individuals' marital gains in their utility compared to their utility available outside of the union. This utility of the alternative state represents a *threat point" beyond vhich the partner would leave. It thus limits the consumption allocations wlthin the family that are acceptable to both spouses. Nonearned income of **the** husband or of the wife will, thus, influence the "threat point" of that **spouse:** it leaves the spouse less dependent on marital gains. It thus strengthens the spouse's bargaining power, and potentially changes the distribution of consumption in the family. 4 This would be most obvious in its effect on the consumption of leisu,^, since this is thought of as a private good. The effect on other forms of consumption may be more ambiguous, such as expenditures on-tobacco, alcohol, toys or apparel, for there is nothing to prevent wealthier women or men from deriving satisfaction from varied consumption activities.

Consequently, nonearned income (or its sources) should be divided into those elements brought to the marriage or accumulated during the marriage through distinct individual activities, the receipt of bequests or transfers, or other personal connections. A wife's nonearned income, such as she might have inherited or brought to the marriage as a dowry, might be expected to reduce her market labor supply more than the same amount of nonearned income 5 brought to the marriage by her husband. Conversely, the payment of a brideprice in many areas of subSaharan Africa by **the** groom to the bride's parents may be associated with the bride increasing her supply of time to the labor force. 6 This prediction of the individualistic bargaining model received only modest support from its first empirical test against U.S. household data (Horney and McElroy, 1988). Subsequent study of the allocation of time of
The evidence thus far suggests that while there is a significantly positive partial correlation between disbursements on IDA lending and debt service paid to IDA, we do not think that this correlation should be interpreted as evidence of defensive lending. There is however a further possibility for defensive lending: it is possible that defensive lending is not done overtly, by increasing disbursements beyond what is prescribed by the PBA formula to countries with incipient debt servicing difficulties. Rather it could be done more covertly through manipulation of the variables driving PBA allocations.<sup>23</sup> In particular, it is possible that defensive lending is accomplished by giving better-thanwarranted CPIA scores to countries with incipient debt servicing difficulties. This would result in higher-than-warranted commitments and eventual disbursements to the country, but yet would not evidence itself as a significant partial correlation between disbursements and debt service after controlling for policy performance.

A simple way to test for this possibility is to examine whether measures of country indebtedness have predictive power for IDA country performance ratings after controlling for other measures of policy performance. We do this in Table 4, where we use data from the Worldwide Governance Indicators project (Kaufmann, Kraay, and Mastruzzi (2006)) as alternative measures of policy performance. We consider two of the six measures from this dataset, Rule of Law, and Government Effectiveness, as ones that correspond most closely to what the IDA country performance ratings are measuring.<sup>24</sup> Although these indicators have the advantage of covering all IDA borrowers, they have a more limited time dimension and are available only 1996, 1998, 2000, and annually since 2002. We then report results of pooled and fixed-effects regressions in this shorter panel, including in turn the stock of IDA debt and the flow of IDA debt service as proxies for incipient debt servicing difficulties that might be met with

> this interaction to be statistically significant, possibly because we consider a much larger sample of IDA recipients and not simply the set of African countries that they study.

> 23 This approach is closely related to that of Celasun and Ramcharan (2006) who study whether the IMF dilutes its lending standards by imposing weaker program conditions or by waiving conditions for countries whose obligations outstanding to the IMF represent a bigger share of the institution’s overall balance sheet.  They find this type of dilution is present in loans financed by the IMF's own resources, where incentives for defensive lending are stronger.

> 24 The WGI indicators are composite indicators averaging information on perceptions of governance and institutional quality from a large number of distinct respondents.  One of the ingredients of the WGI is the CPIA itself, which contributes to the strong correlation between the IDA policy performance measure and the WGI indicators.  This however does not affect the interpretation of the coefficients on the indebtedness variables.

18
- Anderson, James and J. Peter Neary (2003), “The Mercantilist index of trade policy,” _International Economic Review_ , 44, 627--649.

Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga. (2008), "Import Demand Elasticities and Trade Distortions," _Review of Economics and Statistics_ , 90 (4), 666—682.

Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2009). "Estimating trade restrictiveness indices," _Economic Journal_ , 119, 172--199.

Kee, Hiau Looi, Cristina Neagu and Alessandro Nicita (2013). “Is Protectionism on the Rise? Assessing National Trade Policies during the Crisis of 2008,” _The Review of Economics and Statistics, 95(1), 342-346._ 93
Compared to the general labor market, individuals with some tertiary level qualifications are overrepresented in strictly green jobs while individuals with high school or less are overrepresented in broadly green jobs. Of all workers in strictly green jobs, an estimated 27 percent are university graduates and 9 percent have a post high school qualification (principally a technical or vocational school certificate). This contrasts with only 17 percent of workers with university degrees and 6 percent with post-secondary qualifications in the general labor market. Still, the largest share of strictly green jobs is held by those with a high school or lower qualification (64 percent). This is because, as discussed above, while the majority of strictly green occupations are high skilled, more people are employed in mid-level and low skill elementary jobs in the South African labor market. A high share (59 percent) of the workers in broadly green jobs have less than a high school qualification, while only 8 percent of broadly green jobs belong to university graduates. This is consistent with the above discussion that broadly green occupations include many lower skilled occupations (Figure 6).<sup>31</sup> Figure 6: Share employed in green occupations by education level <!-- Start of picture text --> Green strict Green broad All employed<br>100 100 100<br>27 8 17<br>6<br>80 80 27 80 6<br>32<br>9<br>60 30 60 60<br>59<br>40 40 40 46<br>34<br>20 20 20<br>0 0 0<br>Less than gr12 Grade 12 Other tertiary Graduates<br>Percent<br><!-- End of picture text --> Source: authors’ calculations using PALMS The industries with the highest shares of green jobs are manufacturing, trade, and finance but compared to the general employed population, manufacturing, utilities, and finance sector jobs are over-represented among green jobs (Figure 7). The green jobs in the utility industry are those related to electricity, gas and waste collection and purification. The finance sector enables others to develop and use green technologies, including research and development, renting of machinery and other business activities, computer and related services and financial intermediaries. While finance might not seem like a directly green occupation, other studies have found that it is a sector that records a significant share of green jobs (Valero et al. 2021). This reinforces the idea that quantifying green jobs through the occupational lens allows one to capture green jobs in industries that might not be thought

> 31 Doan et al. (2022) find that, on average, green jobs in Viet Nam require higher qualifications than non-green jobs.

18
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C6.3. SOSD: Social Sustainability Index C6.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.95*** 1***<br>H<br>Kendall tau-b 0.78*** 0.89*** 1***<br>Spearman 0.93*** 0.8*** 0.63*<br>A<br>Kendall tau-b 0.83*** 0.67** 0.5*<br>Spearman 1*** 0.92*** 1***<br>M0<br>Kendall tau-b 1*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C7. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Public assistance indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C7.1. FOSD: Counting Vector

<!-- Start of picture text --> C7.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> C7.3. SOSD: Social Sustainability Index <!-- Start of picture text --> C7.4. Rank correlations<br><!-- End of picture text --> 54
72

- the increased cost of vehicle maintenance; and

- the cost of additional fuel if emission control measures result in higher fuel consumption.

Estimated increase in the cost of vehicles and changes in fuel consumption for various low-emission engine and exhaust treatment configurations are given in Table 37 [ECMT 1990].

Table 37. The costs to consumers of various emission control technologies

|Technologies<br>P|rice increas<br>(%)|e<br>Fuel<br>consumption<br>increase (%)|
|---|---|---|
|Lean burn engine with carburetor and conventional ignition|1.0|-2|
|Pulsair and EGR|4.5|+3|
|Lean burn engine with carburetor and programmed ignition|2.0|+ 1|
|Recalibrated conventional engine with EFI|8.0|+2|
|Lean burn engine and EFI|9.0|-7|
|Lean burn engine oxidation catalyst|4.5|-3|
|Open loop 3-way catalyst carburettor|4.1|+2|
|Lean burn engine - closed loop - EFI variable intake<br>system-oxidation catalyst|15.0|-7|
|Closed loop - EFI - 3-way catalyst|13.0|+3|

Baseline = small vehicle, 1.4 litre conventional carburettor engine meeting ECE 15/04 standard. Source: ECMT [1990] In the United States, a cost model was developed by U.S. EPA to arrive at estimates of the initial cost paid by consumers to comply with the U.S. emission standards. The cost estimates were based on an analysis of the retail price equivalent of each component in the emission control systems used in gasoline-fuelled vehicles. The list of emission control components on each car was obtained from the Application for Certification submitted to the U.S. EPA by automobile manufacturers. Prices and price estimates were obtained from three sources: a study conducted for U.S. EPA, a price survey of dealer parts departments, and direct request to the manufacturers for parts price information. Based on the above, new automobile price increases as a function of increasingly tighter U.S. emissions standards were estimated and are summarized in Table 36. All emissions standards have been converted to the U.S. 1975 test procedure (CV5-75) along with the U.S. compliance programme.
credit when they are in a relatively weaker bargaining position which is supported by evidence from Fisman and Raturi (2004) for Africa, Fabbri and Klapper (2016) for China and Dass et al. (2015) for U.S. firms. For example, Fabbri and Klapper (2016) find not only that weakbargaining-power suppliers are more likely to extend trade credit, providing a larger share of their goods on credit, but that they also offer longer payment periods before imposing penalties and, even then, their most important customers are likely to have overdue payments.

An alternative to the market power view builds from the notion above that trade credit is used to redistribute credit to buyers who lack cash and are credit-constrained by banks, and thus more profitable sellers (who have better access to bank credit) are more likely to extend trade credit. Petersen and Rajan (1997) go further suggesting that these more profitable sellers, who may also enjoy market power as reflected in high margins between sales price and variable costs: “...may have a long-term interest in the survival of the customer firm. This is especially true if the supplier has no potential substitutes for the customer. The supplier then factors in not only the net profit margin on current sales but the present value of the profit margins on future sales when deciding whether to help the customer with credit. In other words, the supplier may want to protect the value of its implicit equity stake in the customer by providing it temporary shortterm financing.” Petersen and Rajan (1997), pp. 664-665.

And in their sample, the trade credit offered by a firm increased with the size of its margin on sales. However, this notion of stronger suppliers offering more trade credit would appear to be at odds with the idea that a supplier in a _weak_ bargaining position (relative to its buyers) is more likely to help a buyer with temporary financial problems because its own prospects are positively related to those of that customer (Wilner, 2000), and with the evidence above showing that suppliers with greater relative bargaining power are _less_ likely to provide trade credit to customers in multiple contexts.

Petersen and Rajan (1997) also emphasized, however, that more detailed data on the relationships between suppliers and customers was crucial for testing theoretical predictions regarding trade credit. And recent empirical evidence using new data sources and more detailed information about the nature of supplier/buyer relationships does help reconcile the supplier market/bargaining power hypothesis with the supplier equity stake hypothesis. For example, 11
# **Data Sources for Industry-Trade-Policy and Country-Level Variables Used in Regressions Presented in Table 3.**

- **_Tariff Index_ :** The composite index was estimated as the first principal component derived from factor analysis of the trade-weighted applied tariffs and their standard deviations within each manufacturing industry. The data come from Nicita and Olarreaga (2006). The cross-country regressions use national averages of the industry averages.

- **_NTB Coverage Rate:_** The variable measures the percentage of tariff lines that are subject to non-tariff barriers, which includes price-control measures, finance-control measures, and quantity-control measures. The data come from Nicita and Olarreaga (2006). The cross-country regressions use national averages of the industry averages.

- **_GDP per capita in 2000_ :** Penn World Tables database.

- **_Average Real Manufacturing GDP growth 1998-2003_ :** The data come from the World Bank’s database on _World Development Indicators_ (WDI).

- **_Stock of patents accumulated during 1963-2000 per worker:_** The variable is the total number of utility patents granted to the first innovator residing in each country and cited in the patent application at the U.S. Patent and Trademark Office (USPTO) during this period. The data come from Lederman and Saenz (2005). The number of workers is the population 15-64 years of age in 2000. These data come from the World Bank’s WDI.

- **_Regulation Index_** <u>:</u> The composite index was estimated as the first principal component derived from factor analysis of the Difficulty of Firing Index, Difficulty of Hiring Index and Starting a Business Time (Days). These variables are the average by country for the years 2003, 2004, and 2005 (although some of these years may be missing for some variables for some countries). The data come from the World Bank’s _Doing Business_ database.

- **_Infrastructure Index_ :** The composite index was estimated as the first principal component derived from factor analysis of the Total Road Length in 2001 (km) (per square km of surf. area) and Main Telephone Lines (per 1000 habitants) in 2001. The data come from the World Bank’s WDI.

- **_Institutional Index_ :** The composite index was estimated as the first principal component derived from factor analysis of the Control of Corruption, Political Stability, and the Rule of Law. The data come from Kaufmann, Kraay, and Mastruzzi (2005).

- **_Business density._** Data are from Djankov et al. (2007). This variable is defined as the number of firms per capita by country.

23
# III. **THE** PERFORMANCE OF AFPs

This section reviews the performance of the Chilean pension system over the first ten years of its existence. The first part of the section reviews the record of the industry as a whole, while the second part focusses on the performance of individual AFPs.

## **3.1** Total Funds

> One of the most important indication of the early success of the AFP system in Chile is the

> accumulation of substantial long-term savings. Total resources of the pension funds increased from less than **1%** of **GDP** in **1981** to **11%** in **_1985_** and **26.5%** in **1990** (Table 4). The average annual rate of growth was 47% over the whole period. It was nearly **80%** between **1981** and **198S,** but

> decelerated to just over **26%** between **1985** and **1990.**

Table 4

||CHILEAN|PENSION|**FUNDS-** TOTA|LRFMOURCES||
|---|---|---|---|---|---|
||Amount*|TOTAL<br>Growth<br>**%**p.a.|**FUNDS**<br>%GDP|**NET**<br>Amount*|**CHANGE**<br>**%GDP**|
|**1981**|68.4|**--**|**0.9**|68.4|**0.9**|
|**1982**|**213.1**|**211.5**|**3.6**|144.7|**2.6**|
|**1983**|**385.7**|**81.0**|6.4|**172.6**|**3.5**|
|1984|**508.3**|**31.8**|**8.6**|**122.6**|**3.3**|
|**1985**|**699.8**|**37.7**|**10.9**|**191.5**|4.7|
|**1986**|**918.0**|**31.2**|13.4|**218.2**|4.7|
|**1987**|**1122.7**|**22.3**|**15.5**|204.7|**5.1**|
|**1988**|**1378.2**|**22.8**|**16.5**|**255.5**|4.5|
|**1989**|1699.4|**23.3**|**19.7**|**321.2**|**6.5**|
|**1990**|2249.4|32.4|**26.5**|**550.0**|**10.9**|
|**1991/6**|**2696.5**|**39.7****|n.a.|894.2**|<br>n.a.|

> * billion pesos of December **1990** ** annualized

> Source: Habitat **(1991)**

## **3.2** Coverage

> As already noted, coverage in the Chilean pension system is far from universal. Indeed, one of the main criticisms of the new system is that its coverage is smaller than that of the old system in

> the early 1970s. Since self-employed people were not covered **by** the old system either and cannot

> account for the fall in coverage, the most likely explanation is the decline in formal employment in the 1970s and early 1980s.

Despite its optional character for some workers, the total number of affiliates increased steadily from 1.4 million in **1981** to **2.3** million in **1985** and to **3.7** million in **1990.** In relation to the

**19**
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _14_ Table 1—Cross-validation results.

||_Linear_|_Cubist_<br>_Cubistsynth_|
|---|---|---|
|10-fold val|idation||
|R<sup>2</sup>|0.60|0.94<br>0.95|
|RMSE|8.72|3.49<br>3.10|
|MAE|6.16|1.75<br>1.62|
|Highest co|untry obs|ervation held out for testing|
|R<sup>2</sup>|0.57|0.82<br>0.94|
|RMSE|11.02|7.53<br>5.00|
|MAE|7.77|4.23<br>2.44|
|Final year|held out|for testing|
|R<sup>2</sup>|0.64|0.87<br>0.97|
|RMSE|9.84|6.01<br>3.55|
|MAE|5.96|2.67<br>1.98|

_Note:_ Prediction performance estimated for various setups. In all experiments, the model is optimized for R<sup>2</sup> and the associated MAE and RMSE are given. _Source:_ Results have been estimated by the author for this paper.

In the second experiment, the holdout sample consists of the highest severity rate observed in each country. This explicitly validates the models for their ability to anticipate an unprecedented value (outside observed history) and is specifically aimed at assessing each model’s ability to extrapolate. This more closely resembles the intended use of forecasting the global crisis risks post 2019 based on 2015-2019 data. It follows the previous work of Celiku and Kraay (2017); Andr ́ee et al. (2020) who argue that for prevention purposes, a model must be able to forecast an outbreak (an extreme event) before it occurs, and thus be validated using statistical measures and holdout data that reflect this use case explicitly. This is opposed to using a standard combination of holdout data and statistical measures that are dominated by no-change events. In the extreme value holdout test, a strong drop in prediction performance is visible for both the standard linear and vanilla Cubist models. While the vanilla Cubist model had an R<sup>2</sup> of 0 _._ 94 in the standard 10-fold cross-validation test, performance drops to 0 _._ 82 when the model is trained without each country’s highest observed prevalence rate and then validated against these difficult observations. The RMSE also more than doubles from 3.49 to 7.53, while the MAE increases 2.5 fold. The performance of the Cubist model that has trained on simulated data is not as badly affected. Drawing from richer memory, the model maintains an R<sup>2</sup> of 0.94, while the RMSE and MAE are respectively 34% and 42% below that of the vanilla Cubist model.

In the third experiment, the holdout consists of the 2019 observation. In this test, the models are thus explicitly validated for their ability to forecast future data, training only on history. This similarly zooms in on the intended use of forecasting. Compared to the basic 10-fold validation exercise, there is again a use those in-sample averages to extrapolate onto unobserved countries, and in some cases, there is only a single prevalence rate observed in the country, which would introduce estimation issues.