rafmacalaba/datause-encoder-data huggingface dataset:
- Their impact on the dispersion of corporate ownership and the development of the Chilean equity market has been limited, to a large extent because of the strict investment rules applied on their equity investments. Their equity holdings in privatized firms accounted for nearly **90%** of their total equity investmtnts.

- Concentration of the pension fund industry is high, although it has fahen considerably in recent years. The 3-firm concentration ratio fell from 74% of total pension funds in **1981** to **63%** in **1990,** while the Herfindahl index dropped from 2220 to **_1577._**

- The industry is contestable with low cost entry and intense competition among incumbent firms. Net corranissions (excluding premiums for disability and term life insurance) fell from **23%** of contributions in the early years to **_15%_** in the past couple of years and from 14% of total assets under management to **2.3%.**

The very success of the Chilean pension reform raises many questions that are of interest to policymakers of other developing countries. What accounts for its success? How extensive is the coverage of the new system? Is participation compulsory for covered workers? What is the level of contributions? What is the level of pensions? Are pensions guaranteed? What is the role of indexation? Is the treatment of workers equitable? How expensive is private management (an important feature of the new system)? What is the role of regulation? What is the impact of the new system on the financial markets? How was the transition implemented and financed? Last, but **by** no means least, what are its macroeconomic implications, especially for the level of national savings and the functioning of financial and labor markets?

This is a long list of questions that cannot all be addressed in one paper. Moreover, the more complex macroeconomic issues are still subject to debate and have yet to be properly assessed. The main purpose of this paper is to discuss the rationale of the basic operating characteristics of the Chilean system (section II) and review the performance of the system over the first ten years of its existence (section **III).** Section IV concludes with a summary of the policy issues and main lessons of the Chilean experience for other developing countries. Although no attempt is made to provide a comprehensive account of the Chilean reform, the paper also addresses briefly some of the wider issuer identified above, especially the fiscal implications of the transition and the implications for the devel.p --.t of financial markets.

ibis paper updates and extends an earlier unpublished World Bank Working Paper on the privatization of the pension system **by** Lacey **(1987).** It also draws heavily on the extensive literature on the AFP system **by** Chilean authors, in particular the papers **by** Arellano (1984), Ariztia **(1991)** and Iglesias **(1990)1.** -- e statistical data are mostly drawn from the publication **by** Habitat **(1991)** covering the first ten years of operation of the AFP system.

> **An** extensive bibliography on the Chilean pension system is contained in Iglesias **(1990).**

2
markets crash and foreign lenders and investors pull out their money. As all potential sources of funds collapse there may be “nothing to redistribute” in terms of trade credit; thus, contraction in the supply of formally intermediated funds lead to contraction in the supply of trade credit finance. Consistent with this argument, we also find that countries that experience a sharper decline in bank credit also experience a sharper decline in trade credit.<sup>5</sup> The remainder of the paper is as follows. Section II describes the data and presents basic descriptive statistics and graphical analysis. In section III we discuss our empirical strategy. In section IV we present the results and in Section V we conclude.

# **II. Data**

# **II.1. Sample**

We study two of the four major crises that occurred during the nineties, i.e. the Mexican devaluation in late 1994-early 1995, and the South East Asia currency crisis in mid-1997 including Indonesia, Korea, Malaysia, Philippines and Thailand.<sup>6</sup> We use the Worldscope database, which contains data on publicly traded firms around the world and represents about 95% of the world’s market value. Since it focuses mostly on the firms for which there is a significant interest of international investors, the sample represents the largest firms in each country. Our study excludes all financial firms.

> reduce it (Gertler and Gilchrist (1994)). Such access to alternative sources of finance in the US is likely to be behind the aggregate increase in trade credit (during monetary contractions) observed by Nilsen (2002). 5 Our results are also consistent with patterns observed in Demirguc-Kunt and Maksimovic (2001). They find that the provision of trade credit across countries is positively correlated with the level of development of financial intermediaries.

> 6 We are not able to include the analysis of the Russian default occurred in 1998 and the Brazilian devaluation in early 1999 due to lack of data. We chose not to include information on China and Taiwan because, even though they also suffered contemporaneous financial crisis, their impact is thought to be less spread and not as pronounced.

5
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# **Figure 2: Schooling and age distributions by year.**

Panel A. Education.

<!-- Start of picture text -->
1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>0 5 10 15 20<br>Years of Education<br>1995 2003 2012<br>Fraction in the population aged 18-65<br><!-- End of picture text -->

Note: The distributions of years of education are calculated for all the individuals over the estimating sample (formal, informal and self-employed of ages 18-65). The year 1995 includes 1995 and 1996 PNAD samples, the year 2003 includes 2002 and 2003 PNAD sample, and the year 2012 included 2011 and 2012 PNAD sample.

**Figure 3: Minimum Wage and the Distribution of Earnings**

<!-- Start of picture text --> Panel A. The evolution of real minimum Panel B. Earnings distributions and minimum<br>wage and earnings: 1995-2012. wage spikes by year.<br>2.03<br>2.0 1.0<br>1.8<br>0.8<br>1.6<br>1.43 0.6<br>1.4<br>1.26<br>1.2 0.4<br>1.14<br>1.0 0.95<br>0.2<br>0.8 0.82<br>0.6 0.0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 4 5 6 7 8<br>Year log(Earnings).2005 Reals<br>Average Earnings Median Earnings Minimum Wage 1995 2003 2012<br>1995=1 kdensity<br><!-- End of picture text --> Notes.

Panel A: Median and average labor monthly earnings are calculated with the estimating sample (formal, informal and self-employed of ages 18-65). Negative earnings and the 99th and 1st percentiles of earnings distribution are trimmed. The minimum wage is the national monthly minimum wage. All series are deflated using 2005 Consumer Price Index (CPI) from the World Development Indicators. Panel B: Kernel density functions for all years use the same bandwidth, 0.7. Source: Pesquisa Nacional por Amostra de Domicílios PNAD.

28
# **Table 6:  Employer Skills Set Preferences**

_An X indicates an employer identified ranked as first a skill that corresponds to the skill set; a * indicates the second ranked skill in the corresponding skill set; a blank indicates the skill set was not the first or second priority of the employer. For those surveys that disaggregate within category, the skill corresponding to that disaggregated category is noted. More than one X in bold or not bold indicates a tie in the preference ranking of the skills, some of which are within the same skills set. The skills under each X is available from the authors._

|**Geographic area**|**Basic skills**|**Higher-order skills**|**Socio-emotional**|**Technical Skills**|**Source**|
|---|---|---|---|---|---|
|Global||||||
|Level||||||
|Global||*|**X, X**||Mourshed, Farell and Barton<br>(2012)|
|Global||*|**X**||Andreasson(2009)|
|Gap||||||
|Global|||*|**X**|Mourshed, Farell and Barton<br>(2012)|
|Global||||**X, X**|Manpower(2012)|
|Regional||||||
|Level||||||
|US||**X**|**X**||Zemsky (1997)|
|Romania|||*|**X**|Balcar(2012)|
|Russia||**X, X**|* (managers)<br>*,*,*(non-managers)|**X**|Vasiliev et al. (2013)|
|Poland|||**X, ***||Arnhold et al.(2011)|
|Macedonia|*||**X**||Rutkowski(2010)|
|LAC|||**X**||Beneitone et al.(2007)|
|LAC||*,*|**X, X**||Bassi et al.(2012)|
|LAC||*|**X**||Ogier(2009)|
|Lebanon||*|**X**||World Bank(2012)|
|Egypt|*||**X, X**||AED, reported in Blom and Saeki<br>(2011)|
|Indonesia|**X(workers)**|**X(managers)**|*||diGropello(2011)|
|Vietnam|||**X, X**||World Bank(2008)|
|Philippines||**X(managers)**|*(managers)||diGropello(2010)|

36
Clark, A.E. (1999) Are wages habit-forming? Evidence from micro-data, Journal of Economic Behavior and Organization, 39, 179-200 Clark, A.E., Etile, F., Postel-Vinay, F., Senik, C. and Van der Straeten, K. (2005) Heterogeneity in reported well-being: Evidence from twelve European Countries. Economic Journal, 115, C118C132 Clark, A.E., Frijters, P. and Shields, M. (2008) Relative income, happiness and utility: An explanation for the Easterlin paradox and other puzzles, Journal of Economic Literature, 46(1), 95-144 Clark, A.E. and Oswald, A.J. (1996) Satisfaction and comparison income, Journal of Public Economics, 61, 359-381 Corazzini, L.E. and Majorano. F. (2010). Reign in Hell or Serve in Heaven? A Cross-country Journey into the Relative vs. Absolute Perceptions of Wellbeing," ISLA Working Papers 36, ISLA, Centre for research on Latin American Studies and Transition Economies, Universita' Bocconi, Milano, Italy.

Corneo, G. and Jeanne, O. (1997) Conspicuous consumption, snobbism and conformism, Journal of Public Economics 66, 55-71 Dalton, H. (1920) The Measurement of the Inequality of Incomes, The Economic Journal, 30 (119), 348-361 D’Ambrosio, C., Frick, R.J. and Jäntti, M. (2009). Satisfaction with Life and Economic WellBeing: Evidence from Germany, Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, 129(2), 283295.

D’Ambrosio, C. and Frick, R.J. (2007). Income Satisfaction and Relative Deprivation: An Empirical Link, Social Indicators Research, Springer, 81(3), 497-519 Diener, E., Diener, M. and Diener, C. (1995) Factors predicting the subjective well-being of nations. Journal of Personality and Social Psychology, 69, 851-864 25
Mexico and the rest of Latin America plunged into a major recession, brought on by overborrowing in the 1970s as a result of extremely low real rates of interest, and by excessive reliance of some countries on oil as an export commodity. When the United States drastically increased interest rates to fight its own inflation, Latin America and other developing countries were caught with high foreign debt to gross national product (GNP) ratios and major interest repayments. Moreover, the steep decline in oil prices worsened the crisis for Mexico and other oil exporting countries (World Bank 1998, 1999a).

The economic downturn in the early-1980s increased underemployment rates and lowered real income and wages sharply. The crisis also ended Mexico's (and Latin America's) import-substitution industrialization and forced the restructuring of Mexico's economy. The debt crisis and restructuring turned Mexico's manufacturing and agriculture sectors toward exporting and away from a protected domestic market. Mexico's average economic growth rate in the period 1959-1981 was about seven percent annually, or approximately four percent per capita. However, from the slow-down in 1983 onwards, growth rates have been much lower, about 2.6 percent annually (a 0.3 percent per capita growth rate). Nevertheless, in the past four years (1997-2001) the rate of economic growth has increased to five percent annually, or three percent per capita.

The peso crisis of 1994 was no different. The crisis caused sharp rises in unemployment, a slowing of employment growth, and a drop in real wages. Real wages did not return to 1985 levels until 1998. Large numbers of workers moved to the informal sector and to rural areas, with establishments of fewer than six employees growing by 6.3 percent in 1995 and establishments of more than six employees growing by only 0.6 percent (World Bank 1999b). At the same time, 7
Note: authors’ calculations based on Financial Statements Analysis of Companies (Non-Banking) listed at Pakistan Stock Exchange.

Despite most productivity growth being accounted for the reallocation of resources away from lowproductivity firms into high-productivity firms, substantial productivity differences persist within sectors (Figure _5_ ). This is revealing, given that the publicly listed firms under analysis are likely to be concentrated at the top end of the productivity distribution. High levels of TFP dispersion within sectors can be a symptom of imperfect markets that prevent the efficient allocation of resources. In an extreme case of perfect competition, low-productivity firms could not compete with highproductivity firms. The latter group will tend to absorb the former group’s resources up to the point at which there is a convergence in productivity. Market imperfections, in the form of firm-specific protection through subsidized access to financing, controlled prices, or some form of market power that prevents competition from having a Darwinian effect on firms, may explain high dispersion of firms’ productivity in narrowly defined sectors.<sup>15</sup> For example, if domestic-oriented firms cannot access inputs at global prices while exporters can, domestic-oriented firms will face technological constraints to upgrading productivity. Similarly, if access to finance is restricted to specific types of firms, this will discourage risky investments needed for productivity growth.

Figure 5 – Productivity differences within and between sectors Note: The middle tick indicates the median TFP in the sector, while the left and right hinges of the box indicate the 25<sup>th</sup> and the 75<sup>th</sup> percentile, respectively.

Within our sample, firms in the top quartile of the distribution of sales are on average more than five times more productive than those in the bottom quartile. A similar pattern is observed within sectors. Figure 5 shows the distribution of estimated productivity by sector. In the textiles sector, for example, firms in the top 10th percentile are 3.5 times more productive than those in the bottom 10th percentile.

> 15 See, for example, Hsieh and Klenow, 2009.

13
Energy demand is insensitive to lower income dummies for rural households, after which energy demand responds positively and significantly with higher income (Table 5). More precisely, energy demand for end-use energy in rural India does not respond to income until after the 5th income decile or the 50<sup>th</sup> percentile. Energy consumption up to this income level can be considered the bare minimum necessary for basic needs such as cooking, heating or lighting. The poverty line can be calculated based on the average consumption of end-use energy for households that at the 5<sup>th</sup> income decile 5. This figure which can be characterized as the energy poverty line for rural India is 3.4 kgOE per person per month (Table 6). The situation is somewhat different for urban areas as the demand for energy increases significantly after the 2<sup>nd</sup> income in decile. This means that for urban areas the energy poverty line based on the average energy at the 2<sup>nd</sup> income decile is 2.4 kgOE per person per month.<sup>13</sup> That the urban energy poverty line is slightly lower than the rural one is probably because of greater access to modern energy and a wider range of available energy services.

The energy poverty headcount is based on the number of people above and below the energy poverty line. Likewise the incidence of more general income poverty is based on the number of people below the expenditure poverty line as defined by the National Sample Survey Organization in India. The goal is to make a comparison between energy poverty and the more general income poverty index.<sup>14</sup> One interesting finding is that for the end-use measure, energy poverty in rural areas at about 57 percent is more than double the rate for urban areas at 28 percent. Energy poverty is much worse than expenditure poverty in rural areas, while the values of the two measures are much closer in urban areas. Thus, the figures suggest that energy consumption by urban households is commensurate with economic status probably reflecting easy availability of

> constructing the decile variables.  The instruments used are the prices of alternative sources of energy, which were not then included in the energy consumption equation.

> 13 The energy poverty line calculated for rural Bangladesh using a similar method is 2.6 kgOE per capita per month (Barnes, Khandker, and Samad 2010).

> 14 Expenditure poverty, estimated using the consumption expenditure from our data, is based on state-level rural and urban poverty lines estimated by the National Sample Survey Organization (NSSO) of the Ministry of Statistics and Program Implementation for 1999–2000 (adjusted by the CPI to reflect 2004–05 figures) (GOI 2007).

20
In comparison to the buyers, our suppliers are relatively small: Almost half the suppliers (weighted by the number of contracts) have less than US$ 100 million in sales and only 20% of suppliers have more than US$ 2 billion in sales. Creditworthiness is also an issue for many suppliers, given that almost two-thirds of suppliers are not investment grade.

Table 2, Panel A shows summary statistics of contract characteristics. We have a wide distribution of contract amounts varying from about US$ 400 dollars to over US$ 6.5 billion dollars. Contracts in our sample are generally very long in duration – the average and median is 60 net days. About 75% of contracts in our sample have net days longer than 30 days, which is longer than the ‘typical’ contract of 30 days previously shown in the literature (Ng, Smith and Smith 1999). For example, 20% of contracts have net days of exactly 30 days, 28% have net days of exactly 60 days, and 17% have net days of exactly 75 days.

About 60% of contracts in our sample are denominated in US dollars, followed by almost 40% in euros; this is approximately consistent with the distribution of buyers in Europe and North America (41% and 59%, respectively, as shown in Table 1).

In our sample, 13% of contracts (or 3,717 in total) offer early payment discounts.<sup>3</sup> Panel B of Table 2 shows summary statistics for this subsample of contracts. We also examine the discount terms, including discount days and discount rate. Almost two-thirds of discount terms are 30 days or less, while 27% are between 30 and 60 days, and 9% are more than 60 days. For example, 20% of discount days are 10 days, 20% are 30 days, and 16% are 60 days. The majority of contracts have a spread of net days less discount days equal to 20 or 30 days (15% and 27%, respectively). The mean ratio of discount to net days is 63%.

There is a surprising relationship between net days and discount days – over 30% of contracts have a spread of exactly one day. This might suggest that discounts can be used simply to encourage prompt payments, or as an implicit price discount, i.e. an alternative to a cut in list prices.<sup>4</sup> The mean and median discount is equal to 2%, and 36% of contracts with a discount have a discount rate equal to 1% or less; and only 8% of contracts with a discount have a discount greater than 2%.

> 3 This is a comparable figure to that obtained using SSBF survey data on U.S. firms, indicating that 20% of firms that use trade credit are offered an early payment discount from their suppliers.

> 4 Anecdotally, large buyers do not pay late fees to their suppliers.

6
# **Tables and Figures**

<mark>Table 1: Poverty Headcount - Synthetic vs. Actual Panel Data</mark>

|<br>Status in<br>|<br>Lo<br>|<br>wer Bou<br>|<br>nd Estim<br>|<br> <br> <br>ates<br>Truth<br>Upp<br><br><br>|<br>er Bou<br>|<br>nd Esti<br>|<br>mates<br>|
|---|---|---|---|---|---|---|---|
|Year[1]|[1]|[2]|[3]|[4]<br>[5]<br>[6]|[7]|[8]|[9]|
|_Panel A: Chil_|_e 1996_|||||||
|PovertyRate<br>|15.16|17.02|16.44|.<br>24.22<br>28.34<br>(20.07,28.37)|23.20|24.24|.|
|_Panel B: Nica_|_ragua 20_|_01_||||||
|PovertyRate<br>|40.63|39.44|40.65|.<br>39.03<br>43.56<br>(34.08,43.97)|41.01|41.25|.|
|_Panel C: Per_|_u 2008_|||||||
|PovertyRate<br>|35.16|34.22|33.57|33.19<br>33.35<br>36.52<br>(30.44,36.25)|34.88|34.81|34.97|
|Obs. Panel A|823|823|823|.<br>823<br>823|823|823|.|
|Obs. Panel B|684|684|684|.<br>684<br>684|684|684|.|
|Obs. Panel C|2,368|2,368|2,368|2,368<br>2,368<br>2,368|2,368|2,368|2,368|
|Data source:<br>ENAHO 200<br>Note: R-squa<br>constrained t<br>Results are<br>show a simpl<br>sub-national<br>interactions<br>Columns [4]<br>poverty. 95<br>based on 50 r|Chile C<br>8 and 2009<br>red is cal<br>o the pane<br>weighted<br>e model<br>controls fr<br>between<br>and [9] ad<br>percent co<br>epetitions.|ASEN 1<br>.<br>culated<br>l sampl<br>using ho<br>with hou<br>om cen<br>househo<br>d retrosp<br>nfidenc<br>|996 and<br>for opp<br>e of hous<br>usehold-<br>sehold ti<br>sus and r<br>ld time<br>ective as<br>e interva|2006; Nicaragua EMNV 2001<br>osite halves of the total Year[1<br>eholds whose heads are betwee<br>level survey-sampling weights.<br>me invariant characteristics. Co<br>egion of birth fixed-effects. Co<br>invariant characteristics and<br>set ownership. Results in colum<br>l between parentheses. Upper|and<br>] sam<br>n 25 an<br>Colum<br>lumns<br>lumns<br>sub-na<br>n [5] sh<br>bound|2005; an<br>ple. Res<br>d 65 ye<br>ns [1]<br>[2] and<br>[3] and<br>tional c<br>ow actu<br>estimati|d Peru<br>ults are<br>ars old.<br>and [6]<br>[7] add<br>[8] add<br>ontrols.<br>al panel<br>ons are|

22