rafmacalaba/datause-encoder-data huggingface dataset:
Second, one may expect that the protectionist response depends on how much policy space is available, so we also introduce an interaction term between crisis and tariff water after correcting for smoke in column 9. The coefficient is small, negative and statistically insignificant, so policy space does not seem to be the constraint on countries protectionist response. (Policy space does not enter the regression by itself because we only have it available for 2008 tariffs, and therefore it is perfectly correlated with the country*product fixed effects.) The last column introduces all these interaction terms together.

# **4. Estimating the 2009 protectionist response: fire in the sky?**

We use the estimates reported in the fourth column of Table 3 to predict the percentage increases in MFN tariffs by country during the year 2009. GDP growth estimates are from the Economist Intelligence Unit. Results are reported in Table 4. On average the economic crisis results in an increase in MFN tariffs of 8 percent. The smallest increase will occur in the Kyrgyz Republic and Togo with a moderate 1.1 percent increase in MFN tariffs. Note that of the ten countries with the lowest increase in protectionism, nine are located in SSA. The low increase in protectionism is partly explained by their small economic size, and also the fact that a small share of their neighbors is predicted to be in an economic crisis.

At the other end of the scale, we predict an increase in MFN tariff of around 10 percent in the United States, Canada and Australia. Note however, that these are countries with very low MFN tariffs so the 10 percent increase will only lead to a moderate increase in absolute levels of protectionism. Moreover, for those countries water appears only in a limited number of tariff lines. The predicted increase in MFN tariffs is 8.3 percent in the European Union. Figure 2 plots the predicted percentage change in MFN tariffs against the log of GDP per capita.

15
<!-- Start of picture text --> Professional Management PROFESSIONAL MANAGEMENT<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>T1 T2 T3<br>Source: LAC SOEs Governance Database, The World Bank, 2009. THRESHOLD LEVELS<br>VALUE 0-1<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 T2 JAM ECU D R BZL COL ARG URU T T MEX BOL C R PER PAR<br><!-- End of picture text --> The Professional Management Index shows the highest percentage of companies at T3 levels.

# **COMPONENT 4: TRANSPARENCY AND DISCLOSURE**

The transparency index measures the existence of mechanisms that allow transparent disclosure of the company’s financial and non-financial information, the involvement of civil society in decision-making, and the independent auditing of SOEs’ accounts. The three Tier analysis indicates that the majority of SOEs have minimum conditions to achieve the open disclosure of their performance and accounts. In the sample, no SOEs fulfill the desirable criteria.

<!-- Start of picture text --> Transparency and Disclosure TRANSPARENCY AND DISCLOSURE<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>T1 T2 T3<br>Source: LAC SOEs Governance Database, The World Bank, 2009. THRESHOLD LEVELS<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 URU PER BZL BOL C R JAM COL MEX PAR ARG ECU T2 D R T T<br>NUMBER OF COMPANIES<br><!-- End of picture text --> Together with the Legal Index, our measure of transparency also shows a large number of companies within T2 levels. Uruguay’s Administracion de las Obras Sanitarias del Estado (OSE), Brazil’s Empresa Bahiana de Aguas e Saneamento S.A. (EMBASA), and Brazil’s Grupo CEEEA are the companies with the highest scores. The most significant heterogeneity is found among T2 companies.

# **COMPONENT 5: PERFORMANCE ORIENTATION**

This component addresses the existence of mechanisms to evaluate the performance of SOEs. We intend to identify those arrangements that allow for a performance-based management of the enterprise. In other words, a management that is oriented towards the fulfillment, and achievement, of objectives and goals. We believe this is one, among many, of the ways to increase state companies’ accountability, particularly because of its orientation towards results. A 18
<!-- Start of picture text --> 60,000<br>50,000<br>40,000 ay<br>Nr J ;<br>30,000 maf a NAAT,<br>20,000 7 iN ! \<br>onwl2 | AA1 pee . NMan SNL a eS 11<br>10,000 are-“\y \Vee 7 1<br>_!my \<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Year of marriage<br>—-—-— Haryana ——— Punjab<br>----- Himachal Pradesh sersssess Uttar Pradesh<br><!-- End of picture text -->

<!-- Start of picture text --> 60,000<br>50,000 A \<br>40,0004 ! \ i<br>~\_/ \Y , j ‘<br>30,000 wo oeOEPv VNfieN UTS/\77 Arn<br>20,000 ete, AN /<br>aT aa<br>10,000 ye NLL?<br>en’<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Year of marriage<br>——— Kerala —---- Karnataka<br>ssvseserees” Andhra Pradesh —-—— Tamil Nadu<br><!-- End of picture text --> <!-- Start of picture text --> 50,000<br>40,000<br>\no RA TON<br>H. a ee<br>20,0007 =. “Kae _ STS S35 we<br>Y7” x \p--7 757 Due ZS<br>10,0,000 _-y/<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Year of marriage<br>—-—-7— Maharashtra —— Gujarat<br>—---- Madhya Pradesh verereeesss Rajasthan<br><!-- End of picture text --> <!-- Start of picture text --> 70,000<br>60,000<br>In<br>50,000 is<br>40,000 rf \<br>/ NaN<br>30,0007 \¥ 7 “NL<br>2 Puree, oN cs<br>10,000 a<br>0<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br>Year of marriage<br>sressereese” Bihar —-—-— West Bengal<br>——— Orissa<br><!-- End of picture text -->
costs. The shortfall is made up in part from electricity revenues.<sup>7</sup> In 2005-2006 fares would have had to be raised by 29.6% for BEST transport operations to break even.

**Table 1. Operating Characteristics, Revenues and Costs of BEST Buses**

||**1990-91**|**1995-96**|**2000-01**|**2005-06**|
|---|---|---|---|---|
|Fleet Strength|2,143|3,057|3,432|3,391|
|Annual Bus Km (Million)|163.4|230.1|244.1|240.3|
|Km/Bus/Day|208.9|206.2|212.0|214.1|
|Fleet Utilisation (%)|82.0|94.1|91.9|90.7|
|Occupancy Ratio (%)|97.1|87.2|55.1|58.3|
|Employees/Bus|14.1|12.6|12.0|11.2|
|Accidents Per 1,000 Km|214|67|45|35|
|Km per Liter (HSD)|3.1|3.0|3.0|3.3|
|Buses Purchased|270|89|185|-|
|Total Earnings (Rs. Million)|1,792.2|4,142.5|6,961.0|8,476.6|
|Earnings/km. (Rs.)|11.0|18.0|28.5|35.3|
|Total Cost (Rs. Million)|2,280.7|4,910.7|8,697.7|10,982.3|
|Cost per km (Rs.)|14.0|21.3|35.6|45.7|
|Profit/Loss (Rs. Million)|-488.6|-768.3|-1,736.7|-2,505.7|
|Depreciation (Rs. Million)|235.4|237.0|313.5|768.5|

Source: Ramasaamy (2006)

# **B. Rail Service in Mumbai**

The suburban railway system in Mumbai is one of the most intensively utilized rail networks in the world. It carries over 6 million passengers daily in five corridors that are managed by two different subdivisions of Indian Railways—the Western Railway and the Central Railway. The services are spread over 303 km of tracks—149 km of mass transit tracks and 154 km of tracks that are shared with long distance trains.

Two of these corridors, operated by the Western Railway, run north from Churchgate terminus parallel to the west coast of the city and cover a route of about 60 km. These corridors, shown in orange and red in Figure 1, are popularly known as the Western Line. Two of the corridors operated by the Central Railway and popularly 7 In 2003-2004, 2004-2005 and 2005-2006, BEST earned a profit from electricity supply; however, the profit fell short of transport operating losses. See Table A.4 in the Appendix.

5
- **I** n terms of population and number of households, Delhi records the highest numbers, and Pune the lowest. Kolkata and Chennai are in the same row (with Kolkata having a marginally higher average size of households), followed by Hyderabad.

- In terms of population growth, Pune records the highest followed by Delhi, Hyderabad, Chennai and Kolkata. The lowest numbers in Kolkata can be attributable to   massive out-migration due to closing down of industries while the highest number in Pune is partly due to its rapid gain in importance in the nineties as an investment destination in Maharashtra with over-saturation in Mumbai.

For the ULBs in the non central cities the major observations are<sup>5</sup> :

- While total area covered by the ULBS in the non central cities is the maximum in Kolkata followed by Hyderabad, Chennai and Pune and the minimum in Delhi; Pune has the highest variation in terms of size of the smaller ULBs whereas Delhi has the least variation.

- The number of households and population figures are the highest for Kolkata (with maximum variation amongst ULBs) followed by Hyderabad.

**Table 3 Proportion of Area and Population of Smaller ULBs**

|UA|Area (%)|Population (%)|
|---|---|---|
|Hyderabad|77|34|
|Chennai|54|22<sup>**6**</sup>|
|Kolkata|79|63|
|Delhi|6|4|
|Pune|35|32|

**Note:** The proportions are in terms of total population and total area of the respective UAs. **Source: Census of India, 2001, Authors’ Computations.**

> 5 In terms of average values, for details see Tables 3 and 4.

6 In Chennai we have considered 8 out of the 36 municipal governments which account for 89 percent of the UA’s population, as well as of the UA’s households, and 59 percent of the UA’s land area.

10
_-_ Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

|**_Box_**_12._**Relative weights of diff**|**erent port charges**<br>**Percentage over total bill**|
|---|---|
|Port tariffs on the use of infrastructure|<br>5% - 15%|
|Berthing services|2% -<br>5%|
|Cargohandling|70% - 90%|
|Consignees|3%- 6%|

_Source:_ Suykens (1996) As a consequence of these behaviors, it can be concluded that port authorities can rise or lower port tariffs with a wide margin without affecting their demand levels. An exception would be the case of a region with fierce competition among ports with similar facilities and inland connections. In that case, it is possible that a slight variation in port tariffs could lead to traffic deviations, and thus render port tariffs as a strategic variable for competition.

In theory, an optimal rule for determining port tariffs for the use of port infrastructure should be to make users pay the marginal costs that they generate. But as with other industries where some infrastructure is publicly used (airports, roads, etc), marginal costs are very small, since they consist only of maintenance and repair expenses. These costs are small when compared with construction costs. Therefore there is a problem of cost recovery if the social optimal pricing rule is employed.

The classical solution to this problem was that the public sector paid for infrastructure costs, and thereafter, the users were charged only the marginal cost. The argument for the optimality of this system is that whether users pay full costs, it is possible that some of them would be driven out of the market, even if they are prepared to pay the marginal costs. But since the option of public sector help for seaports is not available in most countries, there is a need for new solutions to determine port charges. One possible alternative is to use the concept of _long-run marginal cost,_ which keeps the idea of social optimality, and at the same time, achieves full cost recovery (for a more detailed exposition on this concept, see _appendix 3)._ Port tariffs are deternined in practice by rule-of-thumb rules that do not necessarily relate to investment costs, nor to opportunity costs for the use of infrastructure. As a general rule, port authorities seek real return rates on assets of around 8-12% during the economic life of the infrastructure (Drewry Shipping Consultants, 1998).

Tariffs charged on ships for the use of infrastructure are usually dependant on some capacity measure, such as the Gross Registered Tons (GRT) or some other alternative. At some ports with high demand levels, port tariffs on ships can be established on other criteria that better reflects the opportunity cost for the port authority of having a given ship use a piece of infrastructure (for example, at the port of Rotterdam, ship tariffs partly depend on 38
Gregorio Impavido, Alberto R. Musalem, and Dimitri Vittas <!-- Start of picture text --> Figure 3: Pension annual real return and governance<br>5.4 - Korea<br>Philippi lndibMarocco Swadrbna ia.pnpor<br>Sri Lank<br>GI atemal jan,aicA(bnia Costa RI<br>Egypt Equador<br>Veonezuel<br>Zambia<br>-33.1 Uganda<br>4.2 l ' ' 110<br>gov<br><!-- End of picture text --> Source: Iglesias and Palacios (2000) Table 4 presents summary statistics for the data used in Iglesias and Palacios (2000). In the first column of the table the correlation between the annual real rate of return and the governance index is 0.49 and significantly different from zero with a p-value of 2.7%. This is the summary statistics of the relationship depicted in Figure 3. Similar results can be obtained by looking at the third column in the same table. The correlation between the standardized real rate of return of pensions assets and governance is now 0.38 and significantly different from zero at 10%.

**Figure 3: Pension annual real return and governance**

<!-- Start of picture text --> 5.4 Kowea<br>M alaysa<br>Philippi IndiaMarocco Swedruimna,Omapor<br>Sri Lank<br>GI atemal JamaicJa Costa Ri<br>Egypt Equador<br>Venezuel<br>Zambia<br>-33.1 Uganda<br>4.2 ' ' 110<br>gov<br><!-- End of picture text --> Source: Iglesias and Palacios (2000)
# **1 Introduction**

Self-employment and entrepreneurial activities create opportunities for better jobs and higher incomes, leading to overall growth in the wider economy. In the case of low-income countries, they are also among the main paths out of poverty for a large share of the population. Yet, many institutional voids in financial markets, such as weak legal regimes and regulations, absence of credit information systems, or costly insolvency proceedings, lead creditors to limit loans and charge prohibitively high interest rates. As a result, many potential entrepreneurs cannot obtain the needed capital to pursue profitable investment opportunities, resulting in constrained labor market outcomes for the individuals and lower levels of economic development for the country.

One commonly observed strategy to circumvent such constraints is temporary migration to higher income countries where workers accumulate savings at a faster rate and use them when they return home for self-employment and entrepreneurial activities. This paper provides empirical evidence on how such temporary migration episodes are used as common _intermediate_ steps to accumulate the required capital and overcome the institutional voids prevalent on the financial markets for entrepreneurial loans, a relation visualized in Figure 1. Our analysis utilizes a newly collected dataset with detailed information on the economic decisions and activities of 5,000 temporary migrants who returned to Bangladesh. The data are unique in providing the complete work trajectories of the respondents, including their economic activities and outcomes before migration, their expectations prior to departure about earning and saving prospects abroad, migration expenditures, sources of income and savings, and employment histories at the foreign destination. Most importantly, we link these trajectories with their economic activities and earnings after their return, showing how the savings from temporary migration finance self-employment and entrepreneurial investments.

International migration itself shares many common features with classical entrepreneurial investments. Temporary migration typically requires considerable upfront investment to cover the expenses and fees, with the promise of high returns afterwards. At the same time, migration involves risks. Earnings potential abroad may be misjudged or the migrant may be forced to return home prematurely, before a savings target is reached. Although almost all migrants from Bangladesh depart for their destinations with valid work contracts, actual earnings may differ from the initial expectations. Part of the compensation is paid in kind (such as housing or food) and the actual cost or the compensation may deviate from what the contract specifies. In addition, premature job dismissal is quite common and leads to immediate expulsion from the host country. By providing a detailed account of the actual financial costs and returns to temporary migration, we are able to show how it can 2
**9 |** P a g e

to 11 in 2007 (see Table 4). Net exporters accounted for 50 percent of BiH total exports in 2003: four years later their aggregate share in total exports increased to 53 percent, as total net exports increased more than three‐folds from US$ 306 million to US$ 949 million with the export coverage of total goods imports increasing from 31 percent to 43 percent. Their contribution measured in terms of their combined share in the difference between the value of exports in 2007 and 2003 amounted to 51 percent.

**Table 4: Two‐digit SITC sectors with the largest contribution to improvement in foreign trade performance between 2003 and 2007 (in percent and millions of US dollars)**

||Product Name|LSG rate<br>2003‐07|Share in<br>exports<br>2003|Share in<br>exports<br>2007|Index<br>2007<br>2003=100|Export in<br>2007<br>minus in<br>2003|Share in<br>total<br>difference|Net<br>exports<br>2003|Net<br>exports<br>2007|
|---|---|---|---|---|---|---|---|---|---|
|24|Cork and wood|22|10.7%|7.0%|65|179|5.7%|105.0|267.0|
|68|Non‐ferrous metals|25|16.3%|10.0%|61|248|7.9%|124.0|213.0|
|82|Furniture/furnishings|35|8.5%|7.1%|83|206|6.6%|29.0|162.0|
|85|Footwear|**43**|5.3%|5.8%|110|188|6.0%|9.0|94.0|
|28|Metal ores and metal scrap|**58**|2.3%|6.8%|299|257|8.2%|‐20.0|79.0|
|71|Power generating equipment|34|6.5%|5.9%|92|180|5.8%|‐6.0|65.0|
|35|Electric current|31|4.6%|3.8%|83|111|3.5%|47.0|25.0|
|27|Crude fertilizer/mineral|**38**|0.8%|0.8%|108|26|0.8%|0.0|16.0|
|32|Coal/coke/briquettes|**55**|1.3%|3.6%|280|135|4.3%|5.0|12.0|
|21|Hide/skin/fur, raw|13|2.6%|1.0%|40|17|0.5%|22.2|9.2|
|52|Inorganic chemicals|**89**|0.1%|1.4%|1048|56|1.8%|‐9.4|7.2|

Note: the value of net exports equals the difference between the value of a sector’s exports and its imports. Source: Based on UN COMTRADE Statistics.

Sectors that joined the rank of net exporters point to the increase in diversification of BiH export offer as well as to a significant progress in industrial restructuring. The “newcomers” range from exporters of raw materials (metal ores and metal scrap) to process production (inorganic chemicals), and capital and skilled labor‐intensive engineering products (power generating equipment).<sup>4</sup> Two sectors that have significantly reduced their foreign trade balance and may be on the path to become net exporters have been metal manufactured goods (SITC 69) and apparel (SITC 84). The former increased its share in total exports from 4 percent in 2003 to 8 percent in 2007, while the difference between exports and imports of these products fell from US$ 84 million to US$ 10 million over this period. The corresponding data for apparel are as follows: the share contracted from 6 percent

> 4 In the context of the emergence of metals ores and scrap as a net exporter, note the contraction in net exports of other raw materials, skins and furs. The value of its net exports fell from US$ 22 million in 2003 to US$ 9 million in 2007. Both appear to be healthy developments reflecting relative scarcities and changing profiles of further processing.
22

> Feenstra, R.C. (1995) “Estimating the Effects of Trade Policy” in _Handbook of International_

_Economics_ , vol. 3, edited by G.N. Grossman and K. Rogoff, Amsterdam: Elsevier. Harberger, A.C. (1959), “Using the Resources at Hand More Effectively”, _American Economic Review_ 49(2): 134-46, May.

Humphrey, T.M. (1992), “Marshallian Cross Diagrams and Their Uses Before Alfred Marshall: the Origins of Demand and Supply Geometry”, _Economic_ _Review_ , Federal Reserve Bank of Richmond, Richmond VA.

Irwin, D.A. (2006), “Historical Aspects of U.S. Trade Policy,” _NBER Reporter_ (Summer) at <u>http://www.nber.org/reporter/summer06/irwin.html</u> Kee, H.L., A. Nicita ands M. Olearraga (2008), “Import Demand Elasticities and Trade Distortions”, _Review of Economics and Statistics_ 90(4): 666-82, November _._ Kee, H.L., A. Nicita ands M. Olearraga (2009), “Estimating Trade Restrictiveness Indexes”, _Economic Journal_ 119(534): 172-99, January _._ Lloyd, P.J. (1974), “A More General Theory of Price Distortions in an Open Economy”, _Journal of International Economics_ 4(4): 365-86, November.

Lloyd, P.J., J.L. Croser and K. Anderson (2009), “How Do Agricultural Policy Restrictions to Global Trade and Welfare Differ across Commodities?” Policy Research Working Paper 4864, World Bank, Washington DC, March.

MacLaren, D. and Lloyd, P.J. (2008), “Measuring Assistance to the Agricultural Industry in Australia Using a Production Assistance Index”, paper presented at the 52nd Conference of the Australian Agricultural and Resource Economics Society, Canberra, 5–8 February. OECD (2008), PSE-CSE Database (Producer and Consumer Support Estimates, OECD Database 1986–2007), Organisation for Economic Co-operation and Development. <u>www.oecd.org/document/55/0,3343,en_2649_33727_36956855_1_1_1_1,00.html</u>
1980’s. This finding might be indicative of a positive impact of the recent pension reform on poverty, which brought a substantial increase in the coverage of social security in Brazil and had a substantial impact mainly on rural poverty. The 1988 Federal Constitution established the universal right to social security and instituted special eligibility conditions for rural workers under the _Regime Geral da Previdência Social_ (RGPS), Brazil’s public pension system for workers in the private sector. Recent analysis based on the 1996-1997 _Pesquisa sobre Padrões de Vida_ (PPV) survey, found that the proportion of rural households receiving pensions from public institutions averages 30 percent in Brazil’s poorer Northeast, and 24 percent in the Southeast. Also Delgado (1999) and Beltrao _et. al._ (1999) find that the implementation of the 1988 eligibility and benefit criteria has been effective in lowering the incidence of poverty among rural households. Furthermore, there has been a substantial increase in the coverage of social security in Brazil in the last decade, which had a substantial impact mainly on rural poverty, through the _Lei Orgânica de Assistência Social_ ((LOAS), which became effective in 1993. The rural pension takes the form of a monthly payment of one minimum salary paid to claimants above 67 years of age or disabled and with per-capita household income less than one-quarter of a minimum salary. The findings from the probit analyses may indicate that the rural pension scheme has been well targeted.

# **4.3.4. Ethnic Background**

For Rio Grande do Norte and the Northeast region, we find that ethnic background (white, black, mulatto) is an import factor contributing to poverty, controlling for other household head characteristics. Data on ethnic origin is only available since 1988. _Mulattos_ and black _Nordestinos_ have a higher incidence of poverty than their white peers. This is in line with what we had observed from the simple statistics presented previously. That indicates that on average black and mulatto-headed households are poorer than whites. The probit regression findings underline the importance of the ethnic background. There are measurable differences between whites and non-whites, controlling for other characteristics. Family and education variables capture part of the difference found in the simple unconditional mean incomes, but still a large part is due to discrimination or other unexplained individual characteristics of the non-white population group in the NE.

36