rafmacalaba/datause-encoder-data huggingface dataset:
_-_ Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

||**_Box 16._Problems of traditional**|**seaports' institutions**|
|---|---|---|
|*|Political pressures|*<br>Poor marketing strategies|
|*|Excess of bureaucracy|*<br>Lack of funds for investment|
|*|Hierarchies' rigidities|*<br>Excess of personnel|
|*|Lack of clear plans and objectives|a<br>Lack of incentives|
|*|Poor management techniques||

_Source:_ Hochstein (1996) Additionally, it is desirable that the structure of the seaports' system is as decentralized as possible, in order for decisions on a port to be taken by an agency with sufficient knowledge of the problems and the local conditions, and not by a centralized agency. Thus, many countries are starting to decentralize their seaport systems and transform their port authorities into private societies with the ability to issue bonds or equities that can be traded such as those of private companies.

Liberalization seeks to reduce monopolistic situations within seaports. It is based on allowing the free entry of new private operators, which can then compete in some cases with transformed formerly public firms. Privatization, on the other hand, is oriented toward selling those former public firms to private agents, in order to eliminate subsidies, improve efficiency, and make users pay the full costs of services. A partial or complete transfer of assets can be chosen, according to the type of service considered, and the political consideration of the asset involved (e.g. infrastructure is not generally sold to the private sector, but only granted temporarily for its use).

Lastly, modernization of seaport administration implies that it is possible to improve institutions in charge of seaports, by introducing changes in the incentives managers have to perform their tasks as skillfully as possible. Ports' comnmercial and marketing strategies have to be changed for ports to be able to survive in highly competitive frameworks. The use of information technologies (implementation of electronic systems to interchange data) should be a priority for all ports in order to modernize their operations.

During the last decades, there has been a general process leading to the transformation of seaports worldwide, which is changing ports from being entities completely controlled by governments to becoming mixed private-public organizations, or even becoming fully transferred to private hands. As pointed out by Hoffman (1998), this reform is probably more positive for developing rather than developed countries, since ports in the former are in serious need of investments for development and renovation, and governments suffer from a lack of resources. However, transforming ports in developing countries is more complicated, due to the fact that seaports' institutions are generally more prone to political control and corruption. Port workers also have more power in those countries to block or delay the required reforms (as in the case of Brazil, as described above), since they are one of the loser groups in this process.

49
# **_How well supported are the rules by public financial management?_**

# **Public financial management is reasonable in many respects.**<sup>14</sup> The quality of budgets,

forecasts, accounts, and statistics is hard to summarize, but in the region, there are generally

- Functioning budgets

- Medium-term fiscal projections

- Monthly or quarterly in-year budget-execution reports

- Accounts or statistics on fiscal outcomes at the end of the year

- Detailed data on traditionally defined debt and debt guarantees (that is, on the bonds the governments have issued and the loans they have contracted, along with the guarantees they have given of other entities’ bond and loans).

In many of the countries, the basic information is published in a timely and accessible manner. There are also some interesting innovations, such as the use of a fiscal council in Serbia and Republika Srpska and the use of external GDP forecasts in Albania.

# **But there are several problems with fiscal information** .

- _Coverage of public institutions_ . Statistics do not include data on all the public institutions that are part of general government according to standards such as ESA 2010 (EC 2013). For example, the recent IMF Fiscal Transparency Evaluation for North Macedonia estimates that statistics on general government should, but do not, include the activities of 12 state-owned enterprises with spending equal to about 3 percent of GDP (IMF 2018, pp. 13–14). Similarly, the Fiscal Transparency Evaluation for Albania estimated that there were about 27 entities that should have been included in data on general government, but were not (IMF 2015, p. 12).

- _Coverage of fiscal stocks and flows_ . None of the central governments publishes a set of audited accrual-based financial statements according to standards like International Public Sector Accounting Standards or a comprehensive set of statistics like those required by the IMF’s _Government Finance Statistics Manuals_ of 2001 and 2014. None can yet produce reliable estimates of the debt and deficit as defined in the EU’s standards. There are plans to gradually adopt accrual-based accounts, for example in Albania, and to

> 14 Available assessments of public financial management include Public Expenditure and Financial Accountability (PEFA) reports for Albania (World Bank 2018), Bosnia and Herzegovina (World Bank 2014); Kosovo (AECOM 2016); North Macedonia (Wiggins and others 2015); Montenegro (World Bank 2013); and Serbia (World Bank 2015); Fiscal Transparency Evaluations for Albania (IMF 2015) and North Macedonia (IMF 2018), as well as older IMF fiscal ROSCs for some of the countries; and IMF Staff Reports for the countries, which contain comments on the adequacy for surveillance of government-finance statistics in each country. Public Finance (or Expenditure) Reviews also include sections on public financial management, including recent ones for Albania (World Bank 2014), North Macedonia (World Bank 2015, 2018), and Serbia (World Bank 2015).

27
Approach," North American Integration and Development Center, School of Public Policy and Social research, UCLA (January)

- Kats, Isaac (2000),"El Impacto Regional del TLCAN: Un Analisis de la Industria Manufacturera," in Leycegui, Beatriz and Rafael Fernandez de Castro (editors), (2000), _Socios Naturales? Cinco Ainos del Tratado de Libre Comrcio de America del Norte ._ Instituto Tecnol6gico Aut6nomo de Mexico, Mexico D.F.

Krugman, Paul (1993), "It's foreign policy, stupid," _Foreign Affairs,_ Nov/Dec

- Krueger, Anne 0. (1999), "Trade Creation and Trade Diversion Under NAFTA," NBER Working Paper No. 7429 (December).

- Leycegui, Beatriz and Rafael Fernandez de Castro (editors), (2000), _Socios Naturales? Cinco Aiios del Tratado de Libre Comrcio de America del Norte_ . Instituto Tecnol6gico Aut6nomo de Mexico, Mexico D.F.

- Lustig, Nora (2001), "Life is not Easy: Mexico's Quest for Stability and Growth," _Journal of Economic Perspectives,_ vol. 15, no. 1, Winter: 85-106.

- McDaniel, Paul R. (1994), " Colloquium on NAFTA and Tradition: Formulary Taxation in North America Free Trade Zone," _Tax Law Review,_ 49 (Summer).

- McLure, Charles (1986), "Tax Competition: Is What 's Good for the Private Goose Also Good for the Public Gander?" 39 _National Tax Journal,_ pp. 341-48.

- Perez de Acha, Luis Mauel (1994), "An Examination of the Tax Treatment of Dividends in Mexico," _Tax Notes International,_ July 11.

- Rubin, Seymour J. and Dean C. Alexander (1995), _NAFTA and Investment_ Kluwer Law International, The Hague, The Netherlands.

- Shoven, John and John Whalley, (editors) (1992), _Canada-U.S. Tax Comparisons ._ Chicago: University of Chicago Press.

- Studer, Isabel (2000), " El Sector Automotor," in Leycegui, Beatriz and Rafael Fernandez de Castro (editors), (2000), _Socios Naturales? Cinco Anios del Tratado de Libre Comrcio de America del Norte ._ Instituto Tecnol6gico Aut6nomo de Mexico, Mexico D.F.

- Tornell, Aaron and Gerardo Esquivel (1995), "The Political Economy of Mexico's Entry to NAFTA," National Bureau of Economic Research, Working Paper No. 5322, Cambridge, MA

- Trigueros, Ignacio (2000), " El TLCAN y la Situaci6n Macroecon6mica de Mexico," in Leycegui, Beatriz and Rafael Fernandez de Castro (editors), (2000), _Socios Naturales? Cinco_

26
analysis is conducted, which in part reflects differences in mandatory education policies (for both starting age and duration of mandatory public education). Furthermore, some countries provide public subsidies to private schools while others do not, which likely affects the average unitary benefits considered. Some countries have been exposed to events such as civil war and insecurity that affect, among others, the supply of and demand for education services. Other countries have a complex decentralization system that affects the delivery of public education. In other words, there is quite a degree of heterogeneity among the public education sectors across these countries.

There are also a number of issues that need to be considered when interpreting these results. It is worth noting that in the context of this static incidence analysis, larger transfers mean larger costs of education because provision costs are equated one-on-one with education benefits.<sup>23</sup> So, to the extent that beneficiaries from poor households attend public schools in areas with higher provision costs—for example, higher salaries for teachers in rural or isolated schools, or rather, large cities with higher costs of living—will affect the progressivity profile. Also, the extent to which administrative and investment spending tends to disproportionally benefit scarcely populated regions may affect the progressivity of public spending because these resources are allocated to fewer students. Furthermore, results might also reflect that richer families opt out of the public system in favor of enrolling their children in private schools. Finally, richer families spend more on fees, texts, school materials, and other costs than poorer families. The disaggregation of the incidence analysis for Paraguay’s education spending illustrates some of these issues.

**Fig 1 Côte d’Ivoire, 2007 (attending school, ages 6-15) Fig 1a                                                         Fig 1b**

<!-- Start of picture text --> Unitary expenditure on education Unitary expenditure on education<br>by quintiles of income (2007) by quintiles of probability (2007)<br>50 58 64 67 72 54 66 77 88<br>15 32 3 6 16 13<br>-37 -47 -52 -70 -18 -30 -49 -50 -64 -21<br>-90<br>-110<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>120 120<br>40 40<br>US$ US$<br>-40 -40<br>-120 -120<br><!-- End of picture text --> Source: Author’s estimates from INS (2008); Abras et al (2011),World Bank (2008) 23 Because the analysis is static, it does not include future rates of return to education, that is, their true investment side.

16
<!-- Start of picture text --> fe a. Sat<br><!-- End of picture text --> <!-- Start of picture text --> yA / 4<br><!-- End of picture text --> <!-- Start of picture text --> a a a esQQ<br>a A RS Re(QO(<br>a A LS RS SC<br>aS SR (CO(<br>a RS Rs(QO<br>a a a es<br>aS RA (CO<br>a A ES RS eS<br>|<br>aA LS Rs es C<br>aS A RS A<br>aS RA es(COCO<br>SSSS A,SSSs<br>a a a 1 |<br>aa<br>| Z<br><!-- End of picture text -->
**Table A2: Country-Specific Sibling Correlation Estimates (1970 Cohort)**

|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|**Country**|**Sibling**<br>**Corr.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.674|0.013|2271|Benin|0.653|0.020|1028|
|Indonesia|0.659|0.006|12924|Burkina Faso|0.584|0.022|1314|
|Philippines|0.614|0.012|4435|Burundi|n.a|n.a|n.a|
|Vietnam|0.682|0.013|2286|Cameroon|0.608|0.037|399|
|||||Chad|0.743|0.024|658|
|Europe & Central Asia||||Congo, Rep.|0.466|0.050|275|
|Albania|n.a|n.a|n.a|Cote d'Ivoire|0.633|0.030|507|
|Armenia|0.468|0.034|1011|Ethiopia|0.793|0.010|2106|
|Kyrgyz Republic|0.379|0.057|468|Gabon|0.475|0.037|478|
|Tajikistan|n.a|n.a|n.a|Ghana|0.592|0.025|980|
|Turkey|0.550|0.013|3375|Guinea|0.597|0.033|635|
|||||Kenya|0.560|0.022|1904|
|Latin America & Caribb|ean|||Lesotho|0.592|0.024|787|
|Bolivia|0.692|0.011|3329|Liberia|n.a|n.a|n.a|
|Brazil|0.709|0.014|1843|Madagascar|0.824|0.011|1658|
|Colombia|0.666|0.011|4009|Malawi|0.609|0.020|1285|
|Dominican<br>Republic|0.595|0.013|3097|Mali|0.626|0.027|842|
|Guatemala|0.750|0.011|2117|Mozambique|0.526|0.025|1347|
|Haiti|0.697|0.015|1272|Namibia|0.554|0.028|846|
|Peru|0.668|0.007|10144|Niger|0.670|0.053|158|
|||||Nigeria|0.643|0.040|404|
|Middle East & North Af|rica|||Rwanda|0.546|0.021|1521|
|Egypt, Arab Rep.|0.523|0.009|9541|Senegal|0.588|0.026|838|
|Jordan|0.474|0.014|4118|Sierra Leone|n.a|n.a|n.a|
|||||South Africa|0.521|0.025|1325|
|South Asia||||Tanzania|0.559|0.023|1617|
|Afghanistan|n.a|n.a|n.a|Togo|0.446|0.029|1130|
|Bangladesh|0.673|0.009|4275|Uganda|0.648|0.023|876|
|India|0.658|0.006|13288|Zambia|0.664|0.016|1710|
|Nepal|0.638|0.016|1816|Zimbabwe|0.569|0.026|1150|
|Pakistan|0.595|0.009|6202|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1970s birth cohort sample.

40
on the premium through policies that offer “no-claim refunds.” With this type of insurance, policy holders receive part (or all) of their premium refunded to them if they do not make an insurance claim. One example common in developed countries is “whole life insurance,” in which customers pay monthly premiums for life insurance but receive a lump sum of all the nominal premiums paid if they are still alive at a certain age. Customers pay extra for this service, and insurance companies earn returns on loadings and investment of the held premiums.

If people choose “no-claim refunds” policies, they show a preference for using insurance as a vehicle for savings. Similarly, many studies support the importance of the precautionary motive as a primary driver of saving (Karlan et al., 2010a; Rosenzweig, 2001; Fafchamps and Pender, 1997; Carroll and Samwick, 1997; Lusardi, 1998; Guiso et al., 1992). Despite the frequent usage of savings to protect against shocks, the rural poor are generally underinsured against large aggregate shocks such as droughts (Townsend, 1994). In a survey of farmers participating in a rainfall insurance pilot in Andhra Pradesh, 88% listed drought as the greatest risk they faced (Giné et al., 2008). If people are saving primarily to protect against shocks yet these savings are not enough to buffer against the most important risk they face, they might find a savings account with an insurance component especially attractive.

While to our knowledge there are no existing commercial products combining weather insurance with savings accounts, savings accounts offering other types of insurance do exist. In the 1990s the China Peoples’ Insurance Company (CPIC) offered a savings account where customers received various types of insurance coverage instead of interest on savings (Morduch, 2006). Similarly, many banks and credit unions in economies offer accounts that confer modest auto or developed savings renters insurance as benefits. Savings accounts that offer some insurance in lieu of interest (such as the one offered by CPIC described above), and insurance policies offering “no-claim refunds” can be seen as lying along a spectrum between insurance and savings. The CPIC savings accounts are mostly savings, while the “no-claim refunds” policies are mostly insurance. Seemingly there would be scope for these mixtures in many insurance markets, including the nascent market for rainfall risk in developing countries. This paper’s theory and experimental evidence suggest some caution: diminishing sensitivity in the loss domain may mean consumers prefer to 5
# _Regional technology center_

While it lags behind Beijing in knowledge creation and the generation of startup companies in the innovative sectors, Shanghai has a lead in the commercialization of technological innovations. Table 12 compares the patents and technology contracts occurred in Beijing and Shanghai in 2001. The sum of technology contracts in Shanghai is almost double that in Beijing, and is increasing fast in recent years (see Figure 3). The development of manufacturing in the Yangtze River Delta, mainly in the private sector, is raising the market demand for advanced technology services in this region.

**Table 12: The Patents, technology contracts in Beijing and Shanghai, 2001**

||Patents||Technolo|gy contracts|
|---|---|---|---|---|
||Applications<br>Au|thorized|Number of<br>contracts|Amount of<br>money (billion|
|||||Yuan)|
|Beijing|12159|6244|23921|10.6|
|Shanghai|12777|5371|23816|19.1|
|National in total|165773|99278|-|78.2|

_Source_ : Beijing Statistic Yearbook 2002, Shanghai and Statistic Yearbook 2002.

**Figure 3: The Technology Transactions in Shanghai, 1991-2001**

<!-- Start of picture text --> 35000 12<br>30000 10<br>25000<br>8<br>20000<br>6<br>15000<br>4<br>10000<br>5000 2<br>0 0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Number of Cont r act s Sum i n RMB ( bi l l i on)<br><!-- End of picture text --> In fact, Shanghai played a critical role in transferring technology at the beginning of rural industrialization in the early 1980s. Engineers and technicians from large and medium sized state-owned enterprises in Shanghai often went to the township enterprises in rural areas nearby to train workers and provide technical advice. Such technical services were often informal in nature. However, as private enterprises multiplied, more and more companies began to establish 18
- 28 -

panels indicates **that** differences in the assumption concerning the share of urban travel have only a minor effect on the estimate of the road user costs; on the other hand, the results are very sensitive to the estimate of the damaging factor of the heavy vehicles. Whereas the estimated RUC of cars and light buses is about 0.4C per vehicle-km, the estimate for heavy buses is 0.8-1.20 per vehicle-km, for medium trucks: 1. 1-2.0,. Heavy trucks are associated, by far, with the largest costs: 3.1-8.7c per vehicle-km. The prevalence of excess axle loading becomes a major determinant of the heavy trucks' externality.

**Table 12.** The Estimation of Road User Costs (US(/veh.-Km)r

||Percent<br>of<br>urban|Marginal<br>damage<br>costs|Margina<br>c|l congestion<br>osts|Total|
|---|---|---|---|---|---|
||km<br>X1)|(2)|Urban<br>(3)|Interurban<br>(4)|(5)|
|**A. Assumption**|**: Urban tra**|**nsport-**<br>**40% o**|**f annual km,**|**GHA**estimates|of ESAL|
|Car|58|0.11|0.17|0.01|0.29|
|Light bus|34|0.22|0.15|0.03|0.40|
|Heavy bus|7|0.66|0.04|0.06|0.76|
|Light truck|23|0.66|0.10|0.03|0.79|
|Medium truck|18|0.92|0.10|0.04|1.06|
|Heavy truck|_5_|2.94|0.04|0.07|3.05|
|B.**Assumption:**|**Urban tra**|**nsport-**<br>**60% of**|annual**km,**|SWKP estimate|s of ESAL|
|Car|76|0.08|0.23|0.01|0.32|
|Light bus|**54**|0.14|0.24|0.02|0.40|
|Heavy bus|14|1.09|0.08|0.05|1.22|
|Light truck|41|0.14|0.18|0.03|0.35|
|Medium truck|**33**|1.81|0.18|0.04|2.03|
|Heavy truck|10|8.58|0.08|0.07|8.73|

* **Based** on the estimates of **RDE** and MCC per vehicle presented in Table 11. The shares of travel on paved and unpaved roads is 75:25, respectively, and the share of urban **travel** is given by column (1).

To estimate the annual road-user costs to the economy, one needs the data on annual urban and interurban travel. Given the inaccuracy of these data, the estimate is only a crude approximation. **In** Table **13** I apply the estimates of travel (derived in the Appendix) to the estimates of the road damage costs and marginal congestion costs. Given the two estimates
**Figure 2: Patterns in intermediate input sourcing at the aggregate level**

<!-- Start of picture text --> Share of VA + domestic inputs Breakdown of imported inputs in<br>manufacturing<br>1.00 0.50<br>0.95 0.40<br>0.90 0.30<br>0.85 0.20<br>0.80 0.10<br>0.75 0.00<br>1995 2005 2015 EU ROW SSA CHINA OAF USA<br>Manufacturing Agriculture 1995 2005<br>Aggregate Aggregate 2015 Aggregate (2015)<br><!-- End of picture text --> Notes: Own calculations based on the EORA data.

Perhaps surprising is the small and declining share of parts sourced from the United States, which in 2015 only accounted for 2.3% of imported inputs and 0.3% of the full output value. However, this is very much in line with its low importance for the majority of countries in the region. Other African countries (OAF) are much less important for Rwanda than for the average country in the region, but that is intuitive given its relatively large distance from both South Africa and the Northern African countries.

From the information in the rows of the country-specific input-output table for Rwanda, we can learn where its firms sell their output. In Figure 3 we show the results for manufacturing products. On the left, we see that domestic sales in the form of intermediate inputs to other sectors is the most important category, but its importance has gone down over time. The second most important category are domestic sales to final demand, which has picked up the share lost by domestic intermediate input use. In 2015, these two components absorb 57% and 41% of manufacturing output, while only a paltry 2% is exported.

The panel on the right shows exports, but note that the scale of this graph is 10 times smaller than on the left. Virtually all manufacturing exports of Rwanda are intermediate inputs. The importance of exports has increased ever so slightly, from 1.8% in 1995 to 2.0% in 2015, but remains much lower than in the rest of sub-Saharan Africa where exports account on average for 11% of manufacturing output. The overall integration of Rwanda in GVCs remains very limited.

The share of agricultural output that is exported is higher than for manufacturers, but the difference is remarkably small. In 2015, 80% of all agricultural output is sold as intermediate inputs to other sectors in Rwanda (mostly to the food processing industry), while 13% goes directly to domestic final demand. Only 2.8% is exported to Europe, 0.6% to Africa, China, or the United States, and 3.4% to the rest of the world, combining intermediate inputs and final demand.

6