rafmacalaba/datause-encoder-data huggingface dataset:
14 with a financial performance score above 6 (and 88 above 8), only three of these have a liquid stock<sup>**7**</sup> . This confirms that a significant number of firms that are not eligible for AFP investments have a healthy financial structure and profitable operations (in fact 25 firms score 10 out of 10 and another 32 score 9 out of 10) but prefer to maintain their listed stocks within controlled hands. Such preferences also work as deterrents to the development of private equity as the next section will show.

Firms’ access to the equity market could also be enhanced through the vigorous implementation of recent improvements in the legal framework for corporate governance. A 2003 assessment conducted by the World Bank found Chile to score well in its observance of the OECD Principles for Corporate Governance. The so-called OPAs (Ofertas Públicas de Adquisiciones) law, enacted in 2000, introduced fundamental improvements, including tender offer rules, class action procedures, and other minority shareholder protections (especially in case of changes in corporate control); disclosure and board review of related party transactions; increased board authority; and equal treatment of foreign shareholders. Following an extended transition period, the law is taking effect in 2004. The draft Capital Markets II Reform law proposes additional improvements to the corporate governance framework, in line with the recommendations of the mentioned assessment. As the new legislation is implemented, efforts could be directed at enhancing disclosure of the true beneficial owners of companies, ensuring that internal safeguards are in place to prevent insider transactions, and increasing the pool of potential directors that are qualified. This could be complemented with clear legal guidance on board and directors’ responsibilities, as well as education of judges to ensure appropriate and consistent enforcement.

Finally, the inadequate financial statements of closed firms are an obstacle to their issuing securities. The financial statements of closed firms are indeed often very different from those required from public companies, and from those expected by institutional investors. Investment bankers reckon that bringing their accounts to acceptable standards for a public issue (over several historical years) would excessively increase the cost of them issuing securities.

> 7   More than 10 percent of capital listed.
Our previous estimates of global poverty measures revealed a substantial contraction in the incidence of poverty in China over the period 1981-2004; the latest update in Chen and Ravallion (2007) indicates that the proportion of China’s population living below an international poverty line of $1.08/day at 1993 prices fell from 64% in 1981 to 10% in 2004; the number of poor by this measure fell by about 500 million.

This international poverty line was converted to local currency using the 1993 Purchasing Power Parity (PPP) rate for China produced from the country-level price surveys done by the International Comparison Program (ICP). The PPP gives the conversion rate for a given currency into a reference currency (the $US) designed to assure parity in terms of purchasing power over commodities. However, these calculations for China rested on an estimate of the country’s PPP for 1993 that was not based on a 1993 price survey, but rather was an updated version of an older (1986) PPP for China.<sup>2</sup> China’s estimated level of poverty in 2004 was thus rooted in a PPP rate that was almost 20 years old, and even then was not drawn from the ICP.<sup>3</sup> In this light, the new estimates in World Bank (2008) of China’s PPP rate for 2005, based on the ICP price survey for that year, are undeniably important new data. The results for China’s first participation in the ICP have already attracted considerable attention, as they suggest that China’s economy in 2005 is 40 percent smaller than we thought. For example, Keidel (2007) claims that the new PPP for China adds 300 million to the count of that country’s poor. Some observers have gone further to claim that the new PPP casts doubt on the extent of China’s, and (hence) the world’s, progress over time against poverty.<sup>4</sup> All this begs for a more careful scrutiny of China’s new PPP and its implications for the extent of poverty in the country and how much progress it has made against poverty. This paper focuses solely on the implications of the new consumption PPP released by World Bank (2008).<sup>5</sup> Our analysis combines the results of the 2005 ICP with a new compilation of national poverty lines for developing countries and tabulations of the distribution of consumption and income in

> 2 More precisely, the previous PPPs were derived using a bilateral comparison of 1986 prices between the United States and China as documented in Ruoen and Kai (1995).

> 3 Blades (2007) speculates on the reasons why China has been reticent to participate in the ICP.

> 4 For example, the Bretton Woods Project (a NGO) claims that the new PPPs “...undermine the much-trumpeted claims that globalization has reduced the number of people living in extreme poverty” (http://www.brettonwoodsproject.org/art-560008). Also see the similar comments in Pogge (2007).

> 5 For the 2005 round the ICP global office has been housed in the World Bank; the Asian Development Bank was the Asia regional office.

2
# Table 2.1 – Real Prices Movement (1989 = 1)

||_Manufacturin_|_g_|||||||
|---|---|---|---|---|---|---|---|---|
|Year|Food<br>Products|Household<br>Equipment|Other<br>Manufact.|Textiles and<br>Apparel|||||
|1989|1.00|1.00|1.00|1.00|||||
|1992|0.91|0.80|0.85|0.93|||||
|1994|0.96|0.96|0.91|1.01|||||
|1996|0.71|0.69|0.70|0.70|||||
|1998|0.69|0.65|0.69|0.71|||||
|2000|0.61|0.62|0.75|0.74|||||
||_Agriculture_||||||||
|Year|Cereals|Dairy|Fruit|Pulses|Meat|Oils and Fats|Sugar|Vegetables|
|1989|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|
|1992|0.89|0.64|0.83|1.08|0.83|0.83|1.21|1.20|
|1994|0.83|0.64|0.84|1.00|0.78|0.80|1.30|1.06|
|1996|0.95|0.59|0.67|1.17|0.63|0.94|1.26|0.76|
|1998|0.91|0.55|0.72|1.06|0.59|0.75|1.15|0.82|
|2000|0.93|0.55|0.69|0.76|0.55|0.63|1.01|0.76|

Source: Author’s calculation based on INEGI households surveys, 1989-2000.

Table 2.2 – Mexico Average Import Tariff (in percentage)

|||_Manufacturin_|_g_|||||||
|---|---|---|---|---|---|---|---|---|---|
|Year|Average<br>Tariff|Food<br>Products|Household<br>Equipment|Other<br>Manufact.|Textiles and<br>Apparel|||||
|1989|12.89|15.1|14.1|10.5|19.7|||||
|1992|12.50|14.6|13.1|12.1|19.8|||||
|1994|8.56|11.6|4.9|8.3|18.0|||||
|1996|6.12|9.0|3.2|6.5|14.0|||||
|1998|4.49|5.7|1.7|4.1|10.0|||||
|2000|4.58|3.9|0.9|2.5|6.0|||||
|||_Agriculture_||||||||
|||Cereals|Dairy|Fruit|Pulses|Meat|Oils and Fats|Sugar|Vegetables|
|1989||9.22|10.9|19.9|10.0|10.0|11.4|12.8|7.2|
|1992||8.39|10.3|19.9|10.0|10.0|12.0|15.0|9.7|
|1994||8.31|11.1|12.9|0.1|10.0|10.4|13.1|1.7|
|1996||7.25|11.2|8.2|0.1|10.0|7.9|8.5|0.7|
|1998||5.45|10.7|4.4|0.1|9.5|5.5|7.1|0.4|
|2000||3.71|11.5|3.4|0.1|8.7|3.4|4.6|0.3|

Source: Author’s calculation based on tariff data from TRAINS and COMTRADE.

47
# **1. Introduction**

A very strong impetus has recently been given to infrastructure investments in Sub-Saharan Africa. For the period 2008-2010, the Chinese EXIM bank committed around $20 bn in infrastructure for financing railway rehabilitation in Nigeria, Angola as well as building dams in Ethiopia for instance. The African Development Bank will spend over $5 bn in the next three years, of which over 60% in infrastructure (mainly roads, energy and water). The World Bank committed in 2009 more than $7 bn in Sub-Saharan Africa (with almost $1.5 bn in roads). Aid to Africa is planned to double in the near future of which investments in infrastructure are likely to be the bulk of it.

Among infrastructure, roads are considered of first interest to reduce poverty due to the widely accepted consensus that transport infrastructure has a significant, positive and substantial impact on economic growth and poverty as it enhances the connectivity of isolated and remote areas (World Bank, 1994; World Bank, 2009.1; Pomfret, 2006).

In the last decades, there has been a pendulum in aid agencies between investments in infrastructure and in social sectors. The massive investments in infrastructure of the previous decades did not provide the promised results, especially in Africa. Is it likely to change at the time when a scaling up of investments in roads and in infrastructure in general is expected? Despite a recent impetus in investments in roads, transport prices on the continent remain the highest in the world (Teravaninthorn et al. 2008), Africa’s share in world’s trade has decreased and poverty has not declined in most rural areas in Sub-Saharan Africa. The suggested ineluctability of a poverty reduction impact through roads investments has thus to be questioned and is investigated in this paper using the Cameroon case study.

Literature on the poverty impact of roads is relatively abundant. Poverty is generally modeled as a direct function of isolation without relying on any theoretical framework; while Deaton (2009) stresses the importance of correctly defining theoretical mechanisms to test in econometric studies. The impact is usually found to be significant: easier/improved road access does reduce poverty (Deininger and Okidi, 2002; Fan, Nyange and Rao, 2005; Jalan and Ravallion, 2002).

2
|Table R.N1: Summary<br>S|tatistics|||
|---|---|---|---|
|E-file|910|0.4296703|O.4953012|
|Turnover|910|208,000|1650000|
|Employment|909|33.19692|108.2876|
|Productivity|909|3846705|2.00E+07|
|VAT|910|O.51230879|o.5001387|
|ToS|910|0.47396264|H.4995785|
|VAT Threshold|910|0.6967033|0.4599351|
|Computer Usage in TC|910|0.4043956|0.4910446|
|Acces to Internet|910|O.8428571|O.36415955|
|Industry<br>|910<br>|2.056044<br>|Oo.7447224<br>|
|Region|910|4.378022|3.018455|
|Legal Form|910|1.83956|1.183205|
|Bank Acct|910|O.7626374|H.4257003|
|Acces to Finance|909|3.391639|1.200485|
|Corruption|823|3.232078|1.480068|
|Acces to Electricity|909|4.254323|1.100205|
|Availability of Skill||||
|Workers|go?|2. BHRb44|1.324649|
|Political Stability|908|4.547357|O.8166971|
|Acces to Road||||
|Infrastructure|910|2.505495|1.270583|
|TCC in Mrs. (money)|363|56,703|107374.9|
|TAC in Mrs. fimaney)|363|12,239|259673.66|
|TCC in days|363|115|140.9535|
|TACindays|363|22|38.7493|

Ragion (1-8), [nductry (1-2) and Legal Farm (1-9) are categorical variables.TOC variables only includes firms which dao all TC in-hoase. Turnover is in thousands (100),
<!-- Start of picture text --> USDbn issued<br>30<br>—— neces Africa SSS<br>== Europe<br>«=e North America 25<br>== Supranationals<br>= . 20<br>=== Latin America<br>== Oceania<br><!-- End of picture text --> <!-- Start of picture text -->
[Aa "0<br>YP<br>4 5<br>Was 0<br>2007. 2008 2009 2010 2011 2012 2013. +2014 ~= 382015 2016<br><!-- End of picture text -->
**9 Tables**

Table 1: Afrobarometer Round 4 (2008)

|Country|Number of Respondents|
|---|---|
|Benin|1200|
|Botswana|1200|
|Burkina Faso|1200|
|Cape Verde|1264|
|Ghana|1200|
|Kenya|1104|
|Lesotho|1200|
|Liberia|1200|
|Madagascar|1350|
|Malawi|1200|
|Mali|1232|
|Mozambique|1200|
|Namibia|1200|
|Nigeria|2324|
|Senegal|1200|
|South Africa|2400|
|Tanzania|1208|
|Uganda|2431|
|Zambia|1200|
|Total N|26513|

Table 2: Experiences with paying taxes and fees across Africa (Afrobarometer 2008)

||Income Tax|Property Tax|Public Utility Fee|Fee for Service|License Fee|
|---|---|---|---|---|---|
|Ghana|38.83|34.00|65.50|58.58|24.08|
|South Africa|38.63|41.21|63.71|59.29|14.88|
|Mali|33.36|24.68|23.70|63.23|39.77|
|Cape Verde|29.21|34.31|81.13|83.99|17.09|
|Namibia|26.08|28.50|62.92|80.92|23.67|
|Nigeria|25.60|23.62|55.46|60.71|26.98|
|Mozambique|24.44|37.90|44.20|72.56|46.83|
|Kenya|23.07|24.23|42.33|80.43|43.01|
|Botswana|17.50|13.17|61.92|74.42|9.92|
|Zambia|17.43|21.85|39.70|77.23|24.52|
|Liberia|16.92|18.00|14.42|56.25|18.08|
|Uganda|16.34|19.29|49.01|76.14|35.05|
|Tanzania|16.31|26.66|36.01|79.88|23.10|
|Benin|15.00|16.00|47.42|76.67|22.58|
|Senegal|14.11|12.59|61.37|80.05|16.30|
|Madagascar|13.19|31.93|36.30|75.19|38.22|
|Malawi|11.10|7.77|21.20|38.68|18.86|
|Burkina Faso|10.61|13.36|32.92|81.92|34.36|
|Lesotho|8.42|3.83|28.92|44.42|3.00|

37
source (see Figures 4.1 and 4.2).<sup>7</sup> We exclude countries with fewer than five firms, which reduces our sample to 14,207 firms in 69 countries.<sup>8</sup> It is important to highlight a few data limitations. First, our sample only covers listed non-financial firms. In most EMDEs listed firms are usually the biggest companies (by assets) and/or have better access to funding sources. This may bias our results, but we believe that information from listed companies, which generally also follow better accounting practices, nevertheless provides a good proxy of the general health of the overall non-financial corporate sector, in particular with regards to potential banking distress.<sup>9</sup> Second, we do not have information about corporates’ financial information by currency and are therefore not able to accurately assess currency risks and external vulnerabilities. This is a general concern, as neither Bloomberg nor any other data source consistently collects this type of information on a cross-country basis. Finally, our data set does not contain information on derivatives, other risk management tools and counterparties which could be helpful to better understand exposures and transmission channels of corporate risk.

> 7 Coverage across regions and industries is heterogeneous: 38% of companies in the dataset are from EAP region, 26% from ECA, 17% from SA, 7.4% from MENA, 6.7% from LAC, 4.3% from SSA. In addition, 31.3% of the sample are firms from Industrials, 22.5% from Consumer Goods, 13.5% from Basic Materials, 11.6% from Consumer Services, 7.2% from Energy, 5.6% from Technology, 4.7% from Health Care, and 1.2% from Telecommunications.

> 8 The representativeness restriction of at least 5 firms in the database was not considered for calculating the VI at the country-industry level.

> 9 For instance, Gabaix (2011) highlights the importance of the firm’s size to explain shocks to the aggregate output. This author states that idiosyncratic shocks to large firms can lead to nontrivial aggregate shocks as modern economies are dominated by large corporations.

10
# **6.2 Out of Sample Test: Transaction Level Analyses in Finland**

In this section, we provide further robustness analysis in support of our main findings, using transaction-level data from Finland. The transaction-level data set from Finland covers individual investors’ complete trading records. There are approximately two million transactions attributable to 303,235 households over the 1995 to 2003 for which we have complete information for our regression analyses. Similar to the U.S. data set, the Finnish data set reports for each transaction, the number of shares traded and the daily closing price. Furthermore, we observe the initial holdings for each account at the beginning of the data set, allowing us to keep track of the total holdings of all households on a daily basis. There is also additional information regarding individuals’ demographic information, such as age and gender. Unlike in the US, we do not have information about income, occupation, and marital status. We obtain stock and firm characteristics from Datastream.

Table 7 reports summary statistics for stock and investor characteristics for Finland. Only stocks that are traded by households in the data set are considered. Summary statistics are calculated by pooling observations over the 1995-2003 time period. All liquidity measures are annual averages as defined earlier in the text. The results show that our major illiquidity measure – adjusted Amihud ratio (AdjIlliq) - is positively skewed with a mean of 10.61, but a much smaller median of 6.21. The distributions of other illiquidity measures are similarly positively skewed. Roll’s C has a mean (median) of 2.96 (2.30) and Zerofreq has a mean (median) of 21.90% (20.64%). Size is also positively skewed, with the average market capitalization approximately 11 times as large as the median firm size.

The mean (median) household portfolio value is 10,823 (2,079) Euros in Finland. 4% of households trade options, while 33% of the primary account holders are female. Less than one 32
**Table 3: Change in biofuels demand (%) relative to baseline in 2020**

|**Carbon tax**|**U**|**S$10/t**|**CO2**|**US**|**$25/t**|**CO2**|**U**|**S$50/tC**|**O2**|
|---|---|---|---|---|---|---|---|---|---|
|**Biofuels subsidy**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|**0%**|**25%**|**50%**|
|World total|0.5|74.1|267.8|1.2|75.5|270.4|2.7|78.1|275.7|
|High-income|0.2|74.0|268.7|0.6|74.9|270.3|1.6|76.6|273.8|
|Aus-NZ|1.1|76.3|293.2|2.4|79.4|298.1|4.6|83.2|306.2|
|Japan|-0.7|74.9|284.4|-1.7|73.5|280.9|-2.6|71.9|277.3|
|Canada|2.1|71.6|266.1|5.2|78.9|277.1|10.3|87.7|295.3|
|United States|2.9|72.6|240.2|6.9|79.4|252.7|13.3|89.8|272.2|
|France|-0.6|72.7|266.9|-1.5|71.6|264.3|-2.3|70.2|261.5|
|Germany|0.3|73.5|263.6|0.9|74.7|265.9|2.2|76.9|270.7|
|Italy|-0.4|76.0|290.7|-0.8|75.5|289.2|-1.0|75.1|288.3|
|Spain|0.1|76.5|288.8|0.2|77.0|289.7|0.9|78.3|292.5|
|UK|0.3|77.1|287.0|0.4|77.5|287.7|0.5|77.5|287.8|
|Rest of EU & EFTA|0.7|77.9|290.9|1.7|80.0|295.0|3.7|83.5|302.6|
|Middle & Low-income|2.0|74.6|261.6|4.9|79.5|271.3|10.1|88.1|288.1|
|China|8.6|77.4|264.3|19.1|97.5|298.7|33.9|122.3|347.6|
|Indonesia|3.2|73.2|249.2|8.1|81.5|265.0|17.0|96.2|293.2|
|Malaysia|1.0|75.9|271.9|2.2|77.9|276.5|4.3|81.5|283.8|
|Thailand|0.4|62.0|236.3|1.0|66.6|238.6|2.4|68.9|243.3|
|Rest of EAP|1.2|72.3|270.6|2.9|77.0|276.9|5.8|82.0|287.4|
|India|1.2|70.2|242.5|2.4|72.6|247.3|4.7|76.4|255.2|
|Rest of SA|5.0|77.8|284.2|12.2|92.2|311.3|24.0|112.9|355.6|
|Argentina|3.5|73.7|262.0|7.4|81.1|275.3|12.5|89.6|292.5|
|Brazil|0.2|48.6|161.3|0.7|50.9|163.9|1.8|53.0|169.0|
|Rest of LAC|3.2|61.0|212.7|7.9|69.2|225.3|15.8|81.2|246.7|
|Russia|6.7|78.6|272.6|15.2|94.5|301.5|27.5|115.3|342.9|
|Rest of ECA|3.9|80.7|303.1|8.9|90.9|322.9|16.4|104.4|352.6|
|MENA|5.4|83.1|298.8|12.6|96.3|325.4|23.9|115.8|366.5|
|South Africa|5.1|74.0|247.8|11.5|85.3|267.3|21.4|101.2|296.9|
|Rest of SSA|1.5|75.5|271.0|2.5|77.6|275.3|4.6|81.1|282.5|

# **3.2 Impacts on GDP**

Table 4 presents the effect of a carbon tax on GDP with and without subsidies to biofuels. As expected, the carbon tax would reduce global GDP under the carbon tax alone case. For instance, 21
are ften ignored. Feedback from farmers is poor. Organizational problems are severe (especially lack of accountability), and operational fimding for extension workers is low.

Much effort was made in the 1980s to implement the training and visit extension system (T&V). It appears that the system had only modest success. Measures of farner contact do show an increase, and extension services may have had impact on increasing pesticide use. However, Husain et al (1994) could find little evidence that T&V had improved the quality of extension advice. An increase in extension contact does not necessarily show that extension has aided growth. In addition, contact is highly skewed towards large farmers. A survey of extension contact in Punjab in 1986 k showed that 60 percent of farmers with over 10 hectares of land had contact with the extension service in the previous year, whereas only 24 percent of farners with _5_ to 10 hectares had contact over the same period.

# IV. **Future Outlook: How to sustain or even improve past growth in agriculture?**

As noted, the past growth rate can not be sustained without substantial increase in the overall productivity of the sector and increase in productivity will require major changes in systems and policies for agriculture. What are those needed changes? This is the fimdamental question facing Pakistani policy makers in agriculture. Answering them will mean first defining - the appropriate role for government to encourage the development of a smoothly functioning market, through institutional and regulatory reforms that facilitate private sector activities and market efficiency. Where market failure is not an issue, and government inefficiency is clearly evident, the strategy will require that government's role must be reduced. Investment and public expenditure on agriculture will need to be reshaped. Government spending needs to focus on public goods and market failures, and not on activities better suited to the private sector. in poverty and the environment, the govermnent will have an active role.

# Needed **f=nns**

Price and Trade Policy: In keeping with structual reforms, output prices should be market determined, so market signals will be transmitted to farmers with least distorton. The best option for the govenmnent is to remove price supports and controls, because these distort market signals and have huge fiscal costs. Such liberalization will generally improve production incentives for crops in which Pakistan has a comparative advantage. The government aim of reducing inter- and intra- year pnce fluctuations can be achieved by other more cost effective means, such as promoting on-farm storage, private-sector storage, and fitures trading. The govemment will need to consider ending the subsidy on wheat imports. Since the price of flour is already market-determined and close to import parity, this will have little effect on consumers, but producers may get prices close to import parity. The govermnent will need to ensure that there are no import restrictions on wheat and flour. It would be desirable to remove protection of Byerlee (1994).

26