rafmacalaba/datause-encoder-data huggingface dataset:
1980s, as described below, did not halt the steady loss of market share,' the growing financial deficits, and in some countries, the impossibility of raising the low productivity indices of the industry. Thus, more radical restructuring processes were put into practice.

# **Box 2.1. Market shares of different transport modes** (1970-1994).

<!-- Start of picture text -->
|4 i% 5.2 65. -69o3- 76.2<br>Rafi(%) 31.3240~ 23.02n 1.<br><!-- End of picture text -->

Source: CEMT. _Evolution des Transports. OECD Countries._ (1970-1994).

# 2.1. - **Consequences of the traditional policies on the rail industry.**

During the past fifty years, the most common structure of the rail sector in many countries was the existence of a single State-owned firm, entrusted with the unified management of both the infrastructure and the rail services. Despite some differences in their degree of commercial autonomy, the traditional methods of regulation and control of this sort of company have been relatively homogeneous. In general, it was assumed that the monopoly power of the national company required price and service regulation to protect the general interest. In addition, there was an obligation on the part of the companies to meet any demand at those prices. The closure of existing lines or the opening of new services required government approval. Thus, competition was rare and often discouraged, and the preservation of the national character of the industry was considered the key factor governing the overall regulation.

Due to this protective environment, most national rail companies incurred growing trading deficits during the 1970s and 1980s. Furthermore, social obligations to their staff made it nearly impossible to reach any agreement on redundancies or even wage adjustments. In some countries, the companies were forced to finance their deficits by borrowing, so that their accounts came to lose all resemblance to reality. Thus, the main problems associated with the traditional policies on railways were: (i) increasing losses on the companies' trading account, which were usually financed via public subsidies; _(ii)_ a high degree of inefficiency in management; and _(iii)_ a business activity oriented exclusively toward production targets, rather than commercial and market targets.<sup>7</sup> These distortions did not come from an artificial reduction in the range of services provided, nor from excessively high fares, but, more commonly, from an unjustified increase in the supply of _7_ On this point, Oum and Yu (1994) and Gathon and Pestieau (1995) have empirically showed that the companies that achieve greatest efficiency were those that had been run with a higher level of autonomy and independence from State intervention.

**9-**
|**Table 3:**<br>**Confirmation perce**<br>**to teacher utility.**|**ntages of various educa**<br>|**tional inputs sorted b**|**y direct importance**|
|---|---|---|---|
||1<br>| Number of studies|Positive and<br>significant relation|Confirmation<br>Percentage|
|**Primary Schools:**||._.||
|Teacher's salary level|11|4|36.4|
|School teacher pupil ratio|26|9|34.6|
|Teacher's years of schooling|18|9|50.0|
|Teacher's experience|23|13|56.5|
|Class instructional time|17|15|88.2|
|Class frequency of homework|11|9|81.8|
|School library|18|16|88.9|
|School textbooks|26|19|73.1|
|**Secondary schools**||||
|Teacher's salary level|11|2|18.2|
|School teacher pupil ratio|22|2|9.1|
|Teacher's experience|12|1|8.3|
|Class instructional time|16|12|75.0|
|School textbooks|13|7|53.8|
|,Source: Fuller and Clarke (1994)||||

If marginal product per dollar were equalized, there is no compelling reason to believe that the confirmation ratios would not have been roughly equal across inputs. While this evidence is far from airtight, as it is possible the differences in precision could have affected the pattern of statistical significance, the most straightforward way to read this evidence is that studies that use non-experimental data systematically evaluate the marginal product of inputs at points that are not education output maximizing. The results suggest that at the input spending allocations typically observed in non-experimental data in which these allocations are the result of choice, the marginal product of inputs is lower for those inputs more highly valued by teachers and hence they are more likely to be statistically insignificant.

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(4) We use this method to calculate net inflows for both Chilean and US mutual funds. In the case of pension funds, we compute this measure by aggregating daily data on net inflows into each fund directly collected by the Chilean Superintendency of Pensions.<sup>26</sup> The results are displayed in Figure 11. Panel A shows the cumulative distribution of net inflows _k_<sup>_I_</sup> _t_<sup>relative to fixed-income assets for Chilean mutual funds and PFAs and</sup> US mutual funds. Negative (positive) values are outflows (inflows). The figure shows that Chilean mutual funds face significant outflows, much more than US mutual funds. For example, the historical probability of experiencing a net outflow of 3% of the portfolio or more is 9% in the case of US multi-sector mutual funds and 33% in the case of Chilean mutual funds. Therefore, the short-termism of Chilean mutual funds relative to US mutual funds might be partly explained by the larger outflows they face. To complement this evidence, Panel B shows the fraction of fixed-term assets held in shortterm assets (up to one and three months) and the probability of outflows of that magnitude.<sup>27</sup> Chilean mutual funds hold 9.3% of their fixed-term assets in instruments with maturity of less than one month, and the probability of an outflow of that magnitude occurring is almost 22%. The estimated figures for US multi-sector bond funds are 3.7% and 6.6% respectively. Thus, according to this estimation, the liquid assets held by Chilean mutual funds would be covering a much more likely outflow than those held by US funds. Therefore, their short-termism might be explained by redemption risk.

> 26 Though not reported, we also computed the monthly inflows using the values and returns derived from our monthly database and obtained qualitatively similar results.

> 27 The values reported correspond to the probability that would be required to have a value at risk (VAR) equal to the fraction of fixed-term assets held by funds at maturities of up to 30 and 90 days. For US funds, we do not have information on the maturity structure at less than three years, so we use the extreme assumption that within the zero to thee year interval, the maturity structure of US funds is proportional to that of Chilean mutual funds.

28
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- Busselle, Rick and Helena Bilandzic. 2009. “Measuring Narrative Engagement.” Media Psychology 12(4):321–347.

- Cettolin, Elena and Sigrid Suetens. 2019. “Return on Trust is Lower for Immigrants.” Economic <u>Journal 129(621):1992–2009.</u>

- Charles, Kerwin Kofi and Jonathan Guryan. 2008. “Prejudice and Wages: An Empirical Assessment of Becker’s The Economics of Discrimination.” <u>Journal</u> of Political Economy 116(5):773–809.

- Crowne, Douglas, Douglas Prescott Crowne and David Marlowe. 1964. The Approval Motive: Studies in Evaluative De endence. New York: <u>p</u> Wiley.

- Dahlberg, Matz, Karin Edmark and Hel ́ene Lundqvist. 2012. “Ethnic Diversity and Preferences for Redistribution.” <u>Journal</u> of Political Economy 120(1):41–76.

- Davis, Mark H. 1980. “Measuring Individual Differences in Empathy: Evidence for a Multidimensional Approach.” <u>Journal</u> Supplement Abstract Service: Catalog of Selected Documents in Psychology 10(85).

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- Dinas, Elias, Vasiliki Fouka and Alain Schl ̈apfer. 2021. “Family History and Attitudes toward Out-Groups: Evidence from the European Refugee Crisis.” The <u>Journal</u> of Politics 83(2):647–661.

- Falk, Armin, Anke Becker, Thomas Dohmen, Benjamin Enke, David Huffman and Uwe Sunde. 2018. “Global Evidence on Economic Preferences.” The <u>Quarterly Journal</u> of Economics 133(4):1645–1692.

28
core disciplines<sup>4</sup> of PTAs - i.e. those disciplines that have a clear economic content, as opposed to other provisions that do not (e.g. _cultural cooperation_ , _anti-terrorism_ ), those that matter for FDI are _investment_ , _intellectual property rights (IPR)_ , _visa and asylum_ , _environmental laws_ , _labor market regulations_ , _movement of capital_ and _competition policy_ . We find that all have a large and positive impact and increase FDI in service-related activities between 32 and 50 percent. However, _labor_ and _environmental_ disciplines decrease FDI in manufacturing-related activities by 48 and 110 percent, respectively, while the remaining disciplines have no significant impact. A plausible explanation is that manufacturing activities tend to employ relatively more low-skill workers and are more polluting than services activities. We believe these two characteristics translate stringent _labor market regulations_ and _environmental laws_ into higher compliance costs, and therefore make these disciplines more binding for manufacturing activities.<sup>5</sup> While we do not test this hypothesis, doing so would require estimates of compliance costs associated with different disciplines in PTAs across different business activities. Finally concerning our third source of heterogeneity, we find that our discipline level results are driven by businesses between developing and developed economies for which the prior regulatory gap is the widest.

The importance of services-related activities and the development gap in driving the positive effect of deep PTAs on FDI is consistent with what the GVC literature has found.<sup>6</sup> One of the reasons for these mirroring responses to deep PTAs is the catalyst

> 4 There are 18 core provisions, which include _tariff liberalization for industrial and agricultural goods_ , _technical barriers to trade (TBT)_ and _sanitary and phytosanitary (SPS) measures_ , _export taxes_ and _anti-dumping_ and _countervailing measures_ , _trade related intellectual property (TRIPs) and trade related investment measures (TRIMs)_ , _movement of capital, state owned enterprises_ , _state aid_ , _competition policies_ , _intellectual property rights (IPR)_ , _investment_ , _public procurement_ and _services_ .

5 Service activities tend not to be adversely affected by labor and environmental disciplines, likely because of the high-skill intensity and intangibility of such activities.

6 Using sectoral data on value added trade, (Laget, Osnago, Rocha, & Ruta, 2018) find that deep PTAs matter more for GVC trade in services and have a larger impact for trade in intermediates between developed and developing countries.

4
expenditures that involves interviews among a subsample of the informal production units identified in phase 1. The paper focuses on a representative subsample of 4,504 informal firms. Table A1 in the appendix defines the variables used in the paper.

# **3.2. Characteristics of the informal sector in DRC**

The informal sector in the Democratic Republic of Congo is heterogeneous in many ways. Differences are noticeable across the individual characteristics of the owners of informal firms (reason for the creation of the firm, educational attainment), but also across the distribution of consumption<sup>3</sup> and capital.

Most people who have created informal enterprises have done so because they lack alternative employment opportunities. Many entrepreneurs started their businesses because they could not find jobs in the formal wage sector. Of entrepreneurs who have created their own businesses because of the lack of jobs in large firms, 63 percent have received secondary education, 65 percent are men while only 35 percent are women (appendix A, figure A.1). The informal sector is dominated by women entrepreneurs, who own approximately 64 percent of all informal firms. This reinforces the finding that, in many developing countries, women manage the majority of small and medium enterprises because they lack access to alternative opportunities (Rubio 1991).

The vast majority of entrepreneurs in the informal sector are poor. Poverty incidence is high among workers in the sector as well. An average 61 percent of informal entrepreneurs are poor, compared with the nationwide poverty incidence, which is 64 percent. However, the incidence of poverty among workers in the informal sector varies dramatically by province. For example, 31 percent of entrepreneurs in Kinshasa are poor, compared with approximately 90 percent in SudKivu (figure 1). There also are striking differences across the provinces in the sociodemographic characteristics of entrepreneurs in the informal sector.

**Figure 1.  Poverty Incidence among informal Entrepreneurs by province, 2012 (%)**

<!-- Start of picture text --> 100 89.43<br>81.23 80.65 79.42<br>80 75.56 73.73 68.15<br>60 54.33<br>44.04 42.87<br>40 30.78<br>20<br>0<br>Sud-Kivu Nord-Kivu Bandundu Kasaï Equateur Province Kasaï Katanga Maniema Bas-Congo Kinshasa<br>Oriental Orientale Occidental<br><!-- End of picture text --> _Source:_ Calculations based on 1-2-3 Survey data.

> 3 The rest of the paper uses consumption as a proxy for income and will use the two interchangeable terms.

6
ratios above one show that, on average, the entrants export more products than their main competitors. In all cases, the entrants export more products than their average competitor; however, again, this ratio is larger for the entrants in products of the _X_ category.

In addition, differences are observed between fresh produce and processed produce. The new products with more entrants are mostly products in the segment of fresh produce (avocado, passion fruit, piquillo pepper, etc.), suggesting that discovery may be easier in these products.

Overall, these results imply that exporters of new products tend to be bigger, export more products, and be experienced exporters, and these results are particularly important for products that are “untasted” in the country, where discovery is likely to be more costly.

Given that in the case of the pioneers in product-markets combinations, we have a larger sample (1,767 observations), we use regression analysis to complement the analysis of the characteristics of the entrants into new markets presented above.<sup>25</sup> We create a dependent variable that is one if a firm is a pioneer in a product-market and zero if the firm is a late entrant into that product-market. We regress that on the size in the year before entry, experience in the product, and experience in the market (an indicator that is one if the firm served that product or market previously). Table 4 presents the results. Using Probit, we find that entrants into new product-market combinations are relatively large exporters (the coefficient of the size of the exporter in _t-1_ is positive and significant). Experience exporting the same product is positively and significantly

> 25 Again entrants are defined in terms of the firms that started to export during the first three years in the lifecycle of a particular new product-market combination. Here we make no distinction between products that are consumed domestically and products that are mainly exported because the focus in this part is on the discovery of markets “new” to the exporters in a particular product regardless of the type of product involved in the transaction.

31
# **_3.2 Randomization and survey sample_**

Before moving on to the results section, we briefly outline the randomization, sampling strategy, and empirical model. The LEAP team asked us to implement a rigorous randomized-control-trial in order to inform an eventual roll-out of this project in the rest of the province, as well as other poor provinces of Cambodia. The pilot was budgeted to run in all villages of seven communes out of a total of 50 communes. Hence the randomization of the LEAP was done at the “commune” level – the lowest administrative level above the village level. Unfortunately, we could not collect baseline data, that is, data before the rollout of the pilot, since we started collaborating with the LEAP team only shortly before the launch of the project. However using baseline data as covariates in the regression models would only influence standard errors (and increase efficiency) and not coefficient estimates given that the program is orthogonal to the error term.

To evaluate a causal effect of the project, we randomly selected 7 communes to receive the LEAP pilot. All villages in the 7 treated pilot communes were treated and also surveyed. In addition, we randomly sampled 18 villages at random from 18 randomly selected control communes. In each of the 36 villages (18 treated, 18 control), we aimed to survey 15 households. We used census lists with household names for each village. In principle, the sample should have a total of 540 households. However we have slightly more (548 households) because we gave the survey team a list of substitute households in case some were no longer present in the village or absent. This happened in a few cases and some households acted as substitutes. Of these 548 some 85 are substitute households (42 control, 43 treated). In 4 cases, the survey team located “missing” households after substitutes had been enumerated, and so the team surveyed both substitute and original households. We checked that the likelihood of absent households was statistically unrelated to living in a LEAP village, which could influence our estimates. Fortunately, this is not the case. The final sample is thus composed of 548 households (272 control, 276 treated). LEAP targeted poor households, those that were officially classified as IDPoor-1 and IDPoor-2. To explore effects across the poor and non-poor, we randomly sampled five households from each of the three official, poverty groups (IDPoor 1, IDPoor 2, Non-Poor). Table A1 in the appendix shows that the samples of households in the treatment and control 11
# Regression model

As in the literature – notably Cantor and Packer (1996) and Sutton (2005) – we postulate a simple linear regression model in the data of the following form: _Sovereign rating =_ α _+ β1(log of GNI per capita) + β2(GDP growth rate)_ _+ β5(Growth volatility) + β6 (Inflation) + β7(Rule of law) + error_ (1) Gross National Income per capita, measured at the current market price, is a proxy for the level of development of a country. The ratio of total reserves to the sum of import and short term debt obligations is a liquidity indicator (originally proposed by Greenspan-Guidotti). A higher value of these variables indicated reduced risk of a default on external obligations. Growth volatility refers to 5-year standard deviation of the GDP growth rate.<sup>9</sup> The Rule of Law variable is taken from a widely used dataset produced and updated by Kaufmann, Kraay and Mastruzzi (2006). This variable, constructed as a function of various governance indicators such as enforcement of property rights and accountability of the government etc, takes value between -2.5 and +2.5 with higher values indicating better governance. (The world average for this variable is zero.) This variable has also been used by Kraay and Nehru (2006).

We also tried fiscal balance as an explanatory variable in our regressions. However, as in Cantor and Packer (1996) and Rowland (2005), this variable was not statistically significant. This may be due to data problems: the definition of the public sector and the reporting standards for fiscal deficit vary greatly from one country to another. To some extent, the fiscal balance is indirectly reflected in the regression through the other variables such as growth, inflation and external debt.<sup>10</sup> The dependent variable is the numeric equivalent of the sovereign long term foreign currency rating from one of the three major agencies - Moody’s, Standard and Poor’s, and FitchRatings. The sovereign ratings issued by the three rating agencies are converted to a numeric scale, with 1 denoting the highest rating (corresponding to AAA for S&P and Fitch, Aaa for 9 For the empirical analysis, data for most of the right hand side variables are taken from the World Bank’s World Development Indicators database and the IMF’s World Economic Outlook database. Data on short term and long term claims are collected from the Bank of International Settlements.

10 We also considered including default history, which has been found to be a significant explanatory variable in several studies (Cantor and Packer 1996, Reinhart, Rogoff and Savastano 2003, Sutton 2005). However, the data on default history is not well defined or does not exist for unrated countries, which typically have no cross-border bond financing and where the data on international bank lending tends to be incomplete. Since our purpose is to predict sovereign rating for unrated developing countries, we include right hand side variables that are readily available from standard data sources for both rated and unrated countries.

12
Figure 2: **Percentage share of free market exports in total world sugar exports** <!-- Start of picture text --> __~~~~~~~~ AI EN WE _<br>70 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~00<br><!-- End of picture text --> Source: See Data _55_ for details Since 1977, total world trade in sugar has fluctuated around 28-30 million MT per annum, in contrast to earlier years during which there were regular increases in the volumes traded . Intemnational trade has stagnated partly due to protection -induced increases in production by importing countries, and partly as a result of the substitution for sugar by lowcaloric sweeteners, especially HFCS in the USA. Declining income elasticities in developing countries and unfavorable economic conditions in a number of the previously centrally planned economies also contributed to lower world trade.

Table 1 shows the principal countries exporting raw sugar. In the 1980s the major exporters were Cuba (3 8%), Australia (1 4%), Thailand _(9.5%)_ and Brazil (7.7%). The relative positions remain unchanged in 1990-93. Together, the total exports of these four countries were around 12 mln MT per annum, out of total world exports of 16.9 million MT during these years. Indian exports have been sporadic and very small if they occurred at all.

Table 1: **Principal exporters of raw sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than_5_mln MT|Cuba|
|1-3 mln MT|Australia, Brazil, Thailand|
|_0.5-0.9_mln MT|Guatemala, Mauritius, South Africa|
|0.2-0.45 mln MT|Colombia, Dominican Republic, Philippines, Swaziland,<br>Guyana, Fiji|

Source: See Data S6 for details Data S6 lists the major exporting countries and their exports for the six years, 1965, 1975, 1985, and 1 990-93. The countries in the table account for about 91 % of world exports. It is interesting to note that over 80% of raw sugar exports come from developing countries.

8