rafmacalaba/datause-encoder-data huggingface dataset:
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# Contract Law

In 1991 Slovenia implicitly adopted the Yugoslav law on contracts, the 0 1978 Law on Obligations.' This law, along with the Law on the Foundations of Property Relations discussed earlier, had replaced the Austrian Civil Code which had previously governed both property and contract relations. The 1978 Law on Obligations did not depart radically from its Austrian predecessor, and its principles fit squarely within the civil law tradition. For example, the generally-applicable sections in Part I of the law provide for freedom of contract and equality of rights among the parties and set out modern rules of offer and acceptance, concepts of capacity and invalidity (on grounds of error, deceit, or duress), notions of consideration (or equivalence of things exchanged), standards for completion, and remedies for breach of contract. The law then provides in Part II special rules for particular types of contracts, including (among other) sale, gift, rent, employment, storage, business representation, insurance, warranty, assignment, and secured and unsecured credit.

Although drafted during the socialist period, the 1978 law included few references to socialist or self-management principles. This was because it was always meant to govern relations between private parties, while relations involving public entities were to be covered by other laws."<sup>0</sup> Thus, major revisions are not needed (as, for example, were needed in Poland"') for the law to provide an adequate framework for private contracts in the postsocialist era."2

# Antimono olv Law

As in other CEE countries, Yugoslav firms were quite large and industry and trade was quite concentrated during the socialist period when compared '09 Zakon o obligacijskih razmerjih (Law on Obligations], Yugoslav Official Gazette, Issue No 29 (1978), most recent amendment Yugoslav Official Gazette, No 39 (1985).

"° Some private contracts in particular areas, such as securities, are also governed by specific legislation. Zakon o vrednostnih papirjih [Law on Papers of Value], Yugoslav Official Gazette, No 64 (1989), with most recent amendment Yugoslav Official Gazette No 29 (1990).

> **"'1** See Gray et. al., Polish Legal Framework, 1991.

> 112 The law does include some provisions regarding the relationship of private obligations to the plan and to self-management agreements. These provisions, to be removed in future amendments, do not interfere with the sections governing purely private obligations.
schools improved their scores only slightly in 2003 and noticeably in 2006 only. Students in the general track would potentially have lower scores in 2003 and similar performance in 2006.

These findings are in line with economic intuition. The short-term effects of the reform could be harmful for general school students who were mixed with low-achievers in newly introduced lower secondary schools. In the longer term, this negative impact disappears. It could be that teachers adjusted their methods to more diversified classrooms or that segregation between and within lower secondary schools recreated the former stratification. Students in mixed-general schools obviously benefited from the reform when one considers general skills tested in PISA. Effects are again more visible in the longer term probably because of similar adjustments and mixing with high-achieving students. Positive effects for vocational school students were expected because after the reform they spend much more time learning nonvocational subjects. However, what is interesting is the enormous magnitude of the effect that is about one standard deviation of PISA international scores. Again, it is not surprising because of the change in classroom time allocated after the reform to the general instead of vocational subjects. However, what is surprising is that counterparts of vocational school students “adapted” so quickly to the new system. In other words, it is striking that just a few additional months of comprehensive instead of vocational education changes general skills for an important number of students so dramatically.

Table 4: Propensity score matching estimates of score change for students in different upper secondary school tracks

|Reading achievement|Score cha<br>PISA 2003 – P|nge:<br>ISA 2000|Score c<br>PISA 2006 –|hange:<br>PISA 2000|
|---|---|---|---|---|
||_Kernel matching_<br>_(1) -(3)_|_1-to-_<br>_1matching_<br>_(1) -(4)_|_Kernel matching_<br>_(1) -(6)_|_1-to-1 matching_<br>_(1) -(7)_|
|All schools|18.8|16.1|35.8|35.0|
||(4.3)|(4.5)|(4.4)|(4.5)|
|ISCED 3C schools|109.2|103.0|126.8|116.9|
||(5.8)|(5.8)|(5.7)|(6.3)|
|ISCED 3B hl|13.0|9.3|28.9|23.4|
|scoos|(5.7)|(6.5)|(5.8)|(7.2)|
|ISCED 3A hl|-17.8|-18.5|-0.4|3.6|
|scoos|(5.4)|(4.3)|(5.1)|(5.0)|
|ISCED 3A d 3B hl|-6.3|-6.6|11.2|6.9|
|an scoos|(4.3)|(4.3)|(4.2)|(4.4)|

Notes: Propensity score matching with common support restriction; Standard errors are given in parentheses and were obtained through BRR method accounting for complex survey design.

Finally, we turn to relative improvement or differences-in-differences estimator of performance change for vocational school students. Relevant estimates are presented in Table 5. They are based on simple calculations from the tables above but clearly show the improvement 20
consequential.<sup>32</sup> Figure A2 compares aggregate inflows as measured by our filled data and from the BOP alone, for total external debt of banks and corporates in our samples of AE and EM. We plot annual here for These show that both series tell the same flows clarity. graphs generally story, but there are periods in which accounting for the missing data makes a significant difference. For advanced economy corporates, a significant expansion leading up to the 2008 crisis and a the subsequent contraction are missed. This is due primarily to filling in portfolio debt data for the US and Spain for the 2008 surge, as well as a few other AE for the earlier 2001 peak. For EM, both banks and corporates had much larger flows relative to the BOP measure following the 2008 collapse, driven primarily by filling data for other Figure A3 plots total external debt inflows for government and central bank sectors. Missing U.S. government portfolio debt drives the difference for the AE in panel (a). EM governments and AE central banks are fairly well represented in terms of volume. Note that net inflows can be negative as well as positive, which is the case for EM central banks, where some missing data consists of negative net inflows, which brings our filled data below the raw BOP total. The surge at the end of the sample for EM central banks is driven by China.

Our dataset captures a large volume of capital inflows by sector that would otherwise be missed. Additionally, our data increases the number of both large and small countries with debt inflow data by sector over a long time horizon at the quarterly frequency.

> 32Note that our inflow sample and outflow sample are not the same, but both samples are balanced panels.

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Tables 15 and 16 show the ratio of assets to liabilities for a selection of private insurance companies. The data begin with 2002, right after the IT crash, through the third quarter of 2007. The ratio in these tables consists of the value of assets related to both the value of guaranteed benefits _and_ not yet granted (but known to policyholders) bonus components of liabilities.

Following the dot.com crash in 2002, the ratio was under unity for all major companies, and significantly so for many. As a result, companies were forced in the immediately ensuing period to reduce bonus commitments not yet granted but known to policyholders on a preliminary basis. By the final quarter of 2005, the ratios of many - but not all - companies were comfortably above unity. Ratios leveled out thereafter through 2007, indicating that they had reached what companies considered to be desirable levels.

**Table 15.  Traditional Premium-based Insurance, with a Guarantee Ratio of Assets to Pension Liabilities. End of year figures.**

||**2002**|**2003**<sup>*****</sup>|**2004***|**2005****|**2006****|**2007**<sup>*******</sup>|
|---|---|---|---|---|---|---|
|AMF Pension|95|105|111|121|120|112|
|Folksam Liv|91|105|107|111|111|108|
|Förenad Liv|98|105|108|111|110|-|
|KPA Pension|91|99|100|100|100|100|
|Länsförsäkringar Liv|86|92|103|114|114|115|
|Nordea Liv|89|93|99|102|-|-|
|Salus Ansvar Liv|88|91|100|101|103|103|
|SEB Trygg Liv, Old|88|95|106|117|122|122|
|SEB Trygg Liv, New|94|99|101|102|100|98|
|Skandia Liv|88|93|101|109|112|114|
|SPP Liv|97|91|94|98|-|-|
|Weighted Average|91|96|104|113|115|112|

*December ** Fourth quarter ***Third quarter  “-“ Indicates the business has been merged with another company. Source. Swedish Financial Supervisory Authority

**Table 16.  Occupational Defined Benefit**<sup>**1**</sup> **Insirance Ratio of Assets to Pension Liabilities. End of year figures.**

||**2002**|**2003**<sup>*****</sup>|**2004***|**2005****|**2006****|**2007**<sup>*******</sup>|
|---|---|---|---|---|---|---|
|Alecta|113|120|128|128|144|164|
|AMF Pension|95|124|137|150|144|156|
|KPA Pension|91|92|101|108|106|102|
|SEB Trygg Liv, New|94|93|100|-|-||
|Skandia Liv|88|105|112|123|156|175|
|SPP Liv|97|111|110|113|-|-|
|Weighted Average||119|126|129|143|161|

1Where the benefit is defined as a percentage of the worker’s salary. *December ** Fourth quarter ***Third quarter “-“ Indicates business has been taken over by other companies. Source. Swedish Financial Supervisory Authority The financial picture for occupational insurance (Table 16) resembled that of individual voluntary insurance in 2002. With the exception of one company, Alecta, long-term pension 41
Overall, the effects of public works on participants’ employment are mixed. On the one hand, they seem promising as most of the studies (8 out of 11) find positive and significant effects on employment at some point in time. Consequently, the overall impact of public works programs on employment is 9.8 percentage points (95 percent CI: 1.9, 17.8). On the other hand, the average employment impacts during the program are 29.3 percentage points. This can be seen as quite low: it means that, for every 100 individuals taking on jobs, 71 would have also worked in the absence of the program. As such, while public works programs do increase the share of individuals employed in the short run, their net effect on the number of participants employed is smaller than the number of positions they offer. In addition, the evidence shows that public works programs do not produce much sustained impacts on employment: the overall impact after the programs end is small (3.4 percentage points) and not significant (p=0.14), with only three of the nine point estimates beyond one-month post-intervention revealing a positive impact.

# **5.2 Earnings**

Figure 4 presents the effects of public works programs on monthly earnings for each study and follow-up survey. We find a positive and significant increase in earnings in at least one point in time in 10 of the 11 studies. Increases in earnings are observed in all cases with an increase in employment, except for the Urban DRC program. Similarly, no effect on earnings is found when there is no corresponding effect on employment, except in Côte d’Ivoire at 12 months, when a small impact on earnings is observed, and in Ethiopia, where the public works opportunity fully crowds out employment in the private sector.<sup>18</sup> Positive effects on earnings are found in all studies measuring impacts during the program, with an average increase of 51.3 USD (95 percent CI: 7.1, 95.4); this is sizable compared to the unweighted control mean of 55.5 USD. The positive effects on earnings are also found to persist up to 12 months after the program, except for Egypt Infrastructure (measured at 4 months) and Djibouti (measured at 9 months). However, positive impacts on earnings are not found for the three studies with follow-up surveys beyond 12 months post-intervention. The overall medium-run (i.e., post-program) effect is an increase of 7.8 USD (95 percent CI: -1.3, 16.8), which is significant at the 10 percent level (p=0.09). Whether a study shows significant earnings gains after the program seems associated with participants’ ability to acquire savings or productive assets for self-employment or business activities, which we will discuss further below.

> 18 In Ethiopia, and as mentioned earlier, the intervention increases public employment by 4.6 percentage points and reduced private employment by 4.7 percentage points. Hence the net effect on employment is zero.

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information on whether firms have overdraft facilities as a measure of access to finance and they also conclude that those firms are more likely to export.

In the second specification, I control for past innovative performance of firms. As a proxy measure for innovation I use the survey question asking whether the firm has developed a major new product line or service in the last three years. It is important to note that the question also includes services. Bernard and Jensen (1999) show that among manufacturing firms in the US, the ones that have recently introduced new products are more likely to export. Introduction of new products or services brings a new source of revenue and firms can have high comparative advantage in these innovations. Results show that innovative firms are more likely to export.

Other factors like the cost of labor and the skill level of the workers can influence the decision of exporting. Cross-country or cross-sector differences in the supply of skill or cost of labor are controlled by the country and sector fixed factors. However, there can still be significant variation in skill levels among firms. To assess how these factors affect the impact of stringent labor regulation on exporting, I include log of wage rate measured as annual wage per employee and the share of non-production workers in the firm. Wage rates could affect export participation in either direction. Higher wage rates can imply lower markups which reduces the comparative advantage of the firm in international markets. On the other hand, higher wages can also imply higher skill level employed by the firm which can put the firm in an advantageous position in export markets. The third specification shows that higher wage payment leads to higher probability of exporting<sup>15</sup> . A similar result emerges when fraction of non-production workers is used as a proxy for the average skill level in the firm in the fourth specification.

Cities with large population might have better investment climates and this can affect firms’ choice of exporting. The infrastructure, customs services, and government services could be more efficient in large cities (unless the city is over populated). The fifth specification uses four dummy variables that represent the population of the city where the firm is located except the capital city which is a separate group. The city groups are: a) capital city, b) larger than 1 Million, c) 250,000-1 Million, d) 50,000-250,000, e) less than 50,000. The omitted group is small cities with less than 50,000 residents. It is seen that firms in capital cities or in cities with

> 15 In this regression, I excluded log of employment as wage rate and employment level are highly correlated. Data for wage rate is only available in 2005 survey.

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6

**where** ln(.) is the natural log operator, a is the intercept, b the elasticity of real government expenditure'} with respect to real GDP, Y, and u is the stochastic error term. All other variables are as defined above.

For the remainder of the paper it will be helpful to keep in mind that **Baumol's** hypothesis implies that coefficient d in equation (3) be greater than unity while Wagner's hypothesis implies that coefficient b in equation (5) be **greater than** unity.

# ESTIMATION RESULTS

Data for 71 countries and for the time period 1970-86 were used to **estimate** the coefficients of equations (3) and (5).13 In 32 countries the **error term** in equation (5) was found to be autoregressive, hence in these countries a generalized least squares procedure was used. Coefficients for equation (5) for all other countries, as well as the coefficients for all countries for equation (3), which showed no cases of autoregressive errors, **weae** estimated with the help of ordinary least squares. Annex Table 1 contains the estimated values of the GDP-deflator elasticity of government-expenditure deflator, d; the real growth rates for total government expenditure and GDP; and the real income elasticity of goverr,ment expenditure, b, for the 71 countries. The rest of this section contains comparisons between the estimated d and the one calculated from other published sources. This section also contains test results for Baumol's and Wagner's hypotheses.

12 It is more accurate to use general government expenditure to measure government size, but since data on this variable are available for very few countries, this study is based on an analysis of central government data contained in the IMF Government Finance data files of the Bank Economic and Social Database (DESD). It should be noted that IMF definition of central government expenditure includes all transfers--to enterprises, other levels of government, households, etc.-- as well as expenditures related to social security schemes that are imposed, controlled, or financad by the government (see IMF 1986, pp. 16, 179-85).

**13** In some countries information for the first or last few years was missing, therefore the sample size ranged from 10 to 17 observations.
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _12_ The food-specific scores are generated as: where _hd_ is constructed in the same way as _h_ , but on the grids _R_<sup>2</sup> _d_<sup>:=(</sup><sup>_R_</sup> _low_<sup>2</sup><sup>_∗_</sup> _R_<sup>2and</sup><sup>_S_:=(0</sup><sup>_, ..., μS_12</sup> ̄<sup>Notethatduetotheharmonic</sup> _d_<sup>_/R_2</sup><sup>_, ..., R_</sup> _d_<sup>2</sup><sup>_, ...,_1)</sup> _N,d_<sup>_, ...,_1).</sup> averaging of data availability in equation 8, the trust score is always guaranteed to be 10 when the data is fully observed even when no data on the covariate side is available.<sup>6</sup> Values below 6 are dis-satisfactory, indicating that the monitor is dysfunctional. Values of 6 _−_ 8 are in the moderate to good range, and values 8 and above imply that price-tracking is very reliable, and values above 9 imply that the impact of any missing data is unnoticeable.

# Intra-month price ranges

Since there is substantial intra-month volatility that is hidden by static monthly price quotes, the price-level estimates are accompanied by intra-month price range estimates. The aim is to be able to compare price changes to their typical shortrun variations, so to put the significance of change into context. Broadly speaking, a 10% change in basket prices would be a significant rate of inflation if the typical monthly variation in prices is less than a percent, but negligible when that typical monthly variation in price is itself 10%. As the application will reveal, fresh produce prices in PNG are particularly volatile so the intra-month price range estimates provide important insights about the price dynamics.

The price ranges are estimated as an Open-High-Low-Close time series object which is defined as: where _P_ is the imputed price series and E∆ _α_ is the expected change in the _α_ - percentile cases. The combined results can be plotted on a candle chart with the majority of price action to have occurred within the body of the candles and wicks indicating the price range between the average of the highest 50% of intra-month prices, and the average price of the lowest 50% of intra-month prices.

The first three quantities in equation 9 are estimated by modeling the time-

> 6In the same way, if the covariates are fully observed but the modeled food item has missing values, the trust score would be 10. This situation is extremely unlikely to ever occur due to the nature of the data.
Analysis of likely trends over the next ten to fifteen years suggests that such an outcome is unlikely, but with the important proviso that fiscal consolidation efforts in industrial countries remain on track. The paper first reviews the prospects for saving and investment in developing countries and their net demand for foreign capital. Next it assesses the prospects for the supply and demand for capital in industrial countries, which are net suppliers of capital to international financial markets. The last section of the paper draws conclusions from this analysis and the main implications for policy.

# **Foreign Capital Requirements of Developing Countries**

It is useful first to place developing countries' net demand for foreign capital in perspective. Private capital flows to developing countries rose sharply in the early 1990s, quadrupling between 1990 and 1994 (Chart 2). The surge in these flows has been a factor feeding the concerns about demand outrunning supply in the global capital market. But despite this rise in private capital flows to developing countries, total net capital flow to these countries remains rather small relative to industrial country financial aggregates. To illustrate, in 1994, total net capital flow to developing countries was approximately $130 billion, about 3 percent of industrial country gross national saving and 0.6 percent of their GDP.

_Demandfor CapitaL_ Looking ahead, the demand for capital in developing countries in the next ten years or so is likely to rise faster than over the past decade. Current World Bank projections are for real gross domestic investment in developing countries to rise at an average annual rate of 6-6.5 percent in 19952004. That would raise their investment to GDP ratio from about 24 percent in 1985-1994 to 27-28 percent in 1995-2004. In absolute terms, investment in developing countries could average $1.7-1.8 trillion per year in 1995-2004 5