rafmacalaba/datause-encoder-data huggingface dataset:
- 5 -

# **II. RECENT DEVELOPMENTS IN TRANSPORT INFRASTRUCTURE**

Tanzania has about 3,557 km of rail lines, which are operated by two rail companies: Tanzania Railways Limited (TRL) and Tanzania Zambia Railway Authority (TAZARA). The TRL lines were constructed during the colonial era in the early 20th century. The construction of the Central Railway was started in 1905, to Kigoma, a port town situated on Lake Tanganyika, which was reached in 1914 (e.g., Amin, Willetts and Matheson, 1986). The current TRL was established as a parastatal company jointly owned by Reli Assets Holding Company (RAHCO) on behalf of the Government of Tanzania (49 percent) and an Indian private operator RITES (51 percent) in 2007. However, the anticipated increase in performance under RITES management did not materialize.<sup>1</sup> Since the completion of renegotiations in 2011, the company has fully been owned by the government.

The TRL network is based on a 1,000-mm narrow-gauge standard and extends more than 2,500 km, connecting Dar es Salaam and large inland cities, such as Mwanza, Kigoma and Arusha. These inland areas are more than 1,000 km away from the coast. Historically, rail transport has been playing an important role to provide affordable access to the global market to Tanzania, which is a large country with a land area of more than 900,000 km<sup>2</sup> .

TAZARA is another major rail line connecting Dar es Salaam to Mbeya and New Mposhi in Zambia. Unlike the TRL, which is primarily a national network with the ends of lines located at Dar es Salaam and Tanga on the Indican Ocean, Mwanza on Lake Victoria and Kigoma on Lake Tanganyika, TAZARA is a binational rail network, extending 1,860 km, with 975 km in Tanzania and 885 km in Zambia ( **Table 3** ). TAZARA is a statutory body established under TAZARA Act No.4 of 1995 (Repealed Act No.23 of 1975) and jointly owned by the two governments, Tanzania and Zambia, on a 50:50 basis. Other neighboring countries, such as Malawi and Southern Democratic Republic of Congo, which are practically landlocked in the 1 Based on the TRL website and TRL (2014). TRL performance review: JTSR 2014.
insignificant impact of the program. The gap actually increased for the treatment and control groups that were followed over both studies, as is evident from the third panel of table 3.

To accommodate these differential changes in productivity levels over time, we used the panel data sets assembled for each study to re-estimate the Cobb-Douglas production functions in first differences, as in equation (2). Two specifications of value added were used; one reported by STPS, the other defined simply as value of production less the costs of intermediate inputs and materials. Production functions, estimated separately for each study, were augmented with a set of industry dummy variables to control for industry effects. For the second study, we included an indicator variable for 1995 to control for the potential negative productivity effects of the economic downturn; we also included 1991-93 data on the sample of firms from the first study that were followed over the 1993-95 period to see if a longer panel would change the estimates of the productivity impact of CIMO participation.

**Table 4. Production Function Estimates for 1991-1993**

||<br>|**Dependen**<br>**Log(Valu**<br>**1991-**<br>|**t variable:**<br>**e added)**<br>**1993**<br>||**Log(Pro**<br>|**Dependen**<br>**duction val**<br>**1991-**<br>|**t variable**<br>**ue - mate**<br>**1993**<br>|**:**<br>**rials cost )**<br>|
|---|---|---|---|---|---|---|---|---|
|Variable|_Leve_<br>Coefficient|_ls_<br><br>z-<br>statistic|_First diffe_<br> Coefficient|_rences_<br>z-<br>statistic|_Le_<br> Coefficie|_vels_<br>nt<br>z-<br>statistic|_First d_<br>Coeffici|_ifferences_<br>ent<br>z-<br>statistic|
|Log(Capital assets)|0.224|* 11.5|0.099|*<br>4.1|0.239|*<br>9.4|0.099|*<br>3.2|
|Log (Employment)|0.828|*<br>27|0.416|*<br>7|0.8|*<br>19.9|0.432|*<br>5.5|
|CIMO Variable|-0.098|-1.6|0.058|*<br>2|-0.184|*<br>-2.3|0.108|*<br>2.9|
|Constant|7.876|* 33.4|0.037|1.2|7.838|*<br>25.2|-0.051|<br>-1.3|
|No. of observations|1533||1017||1516||998||

_Note:_ Industry dummy variables included but not reported. Value added variable was created by STPS.

* Denotes significance at 5 % level.

Table 4 reports the results of estimating two production function specifications for the 1991-1993 period of the first study: one in levels corresponding to equation (1), and one in first differences corresponding to equation (2). As hypothesized, the results **in levels** suggest that CIMO had no impact on productivity levels of participants as compared to the control group; in fact, the θ parameter suggests that CIMO participants had productivity levels that were about 9 to 18 percent lower than the control group. In contrast, when estimated **in first-differences** , the θ parameter switched signs and became positive – 6 to 11 percent, depending on the measure of 12
- 1.18 -

Indicate that the chances for a drought In a particular year In Ghana Is one out of five. At this low risk level, the risk averse farmer can use fertilizer on his farm. Despite Its advantage, fertilizer is used by a small proportlon of the farmers. Only 14.3 percent of the farmers surveyed used fertilizer, and of this number, 66 percent were either maize, sorghum or milet farmers. None of the cocoa and oil palm farmers used fertilizer. As the population grows and food demand increases, Ghana would have to increase the food production o, resort to bIports. With a precarious export earn!ng potential, the viable alternative Is expansion In domestic production. Such an expanslon would put pressure on land and so the bush-fallow system would have to give way to more Intensive cultivation which would require the application of fertilizer to sustain the system. Even without an acute population pressure currently Ghana Is a high cost producer in many food conmmoditles like maize and rice. The cost per unit of output can be reduced substantially If ylelds would Increase. Yields can Increase through the use of fertilizer. If the widespread use of fertilizer can be postponed because of the abundance of land, It can hardiy be neglected In the cultivation of long cycle perennial crops. it is therefore disturbing that none of either the cocoa or oil palm farmers In the sample used fertilizer on their crops.

There Is very little use of insectIcides or fungicides on annual crops. Only 2.4 percent of the annual crop farmers used some sort of insecticide/fungicide. The situation was slightly better for perennials with 14.1 percent of the farmers spraying thear crops with insecticide or fungicide. Although the Cocoa Board recommends that cocoa farmers should spray their farms with Insecticide four tkmes In a year in order to control capsids and thereby Increase thelr yields, only 38.4 percent of the cocoa farmers In the survey spra>-hd their farms and most of them sprayed only once a year.

When the crops are matured, they are harvested. Some of the produce are consumed by the farm household and the surplus Is sold either In the local market or to a wholesaler who will take them to a large urban market. Groundnuts and the cereals
|**Table A.1: Summary Statistics**<br>||||||
|---|---|---|---|---|---|
|**Variables**|Obs|Mean|Std. Dev.|Min|Max|
|**Propensity to pay bribe**||||||
|All including payment w/o receipts|3760|0.49|0.50|0|1|
|For Admission|3760|0.11|0.31|0|1|
|For Scholarship payments|3760|0.04|0.20|0|1|
|All excluding payment w/o receipts|3760|0.18|0.39|0|1|
|Payment w/o receipts|3760|0.40|0.49|0|1|
|Amount of bribe paid annually ('000 Taka)|1832|0.24|0.98|0.01|28.48|
|Monthly Per Capita household income (PCI) ('000 Taka)|3760|2.26|1.94|0.2|31.83|
|PCI of households paying bribe ('000 Taka)|1832|1.93|1.57|0.2|16.00|
|PCI of households not paying bribe ('000 Taka)|1928|2.58|2.20|0.3|31.83|
|Log (total household income)|3760|9.21|0.67|6.91|12.21|
|Rainfall (mean over last 10 years) (milimetre)|3760|1598|423|1009|3299|
|Standard Deviation of Rainfall (mean over last 10 yr)|3760|217|63|89|552|
|**Household Characteristics**||||||
|Household size|3760|5.84|2.17|2|21|
|No. of School age children|3760|2.11|1.06|1|7|
|Age of Head|3760|49.16|13.17|18|110|
|Head female|3760|0.12|0.32|0|1|
|Head Muslim|3760|0.86|0.35|0|1|
|Head's education secondary or above|3760|0.33|0.47|0|1|
|Head's occupation(professional=1)|3760|0.09|0.28|0|1|

Data Source: National Household Survey on Corruption (NHSC), 2010
# **_4.1. Macroeconomic results_**

## _Business-as-usual scenario with no Canal expansion_

The behavior of macroeconomic variables in the BaU scenario is summarized in Table 3. The results are reported separately for two periods: 2003-2014 and 2014-2020, with the first period characterized by rapid growth in real income and the second period exhibiting a marked slowdown to a more sustainable, lower growth path.<sup>13</sup> In the second period, exports growth slows down relative to imports as the real exchange rate experiences a more marked appreciation. This is determined mainly by the dynamics of productivity growth, which drives the strong growth performance during the first period and slows down rapidly in the later years.<sup>14</sup> In the high growth period, increases in productivity help keep output costs down and buttress the competitiveness of Panamanian producers vis-à-vis foreign firms. During the transition to slower growth, smaller annual improvements in labor productivity imply that more workers are needed for a given increase in output, driving up labor costs and eroding the competitiveness of Panamanian products versus foreign-made goods. Finally, as explained in the previous section, public consumption and public and private investment remain fixed as a share of real GDP in every year of the BaU scenario.

## **Table** **<u>3 Macroeconomic variables</u>**

||Initial<br>levels<br>(bn lcu)|Av<br>Ba|erage annua<br>U|l growth (%<br>Can|)<br>al|
|---|---|---|---|---|---|
||2003|2003-14|2014-20|2003-14|2014-20|
|Real GDP at market prices|12,933|5.36|3.06|5.37|3.68|
|Private consumption|8,016|5.40|3.03|5.42|3.92|
|Public consumption|1,807|5.36|3.06|5.36|3.06|
|Investment|2,457|4.92|2.89|5.33|3.08|
|Non-canal investment|2,120|5.36|3.06|5.37|3.68|
|Canal investment|87|5.36|3.06|12.86|-2.36|
|Stock changes|249|0.00|0.00|0.00|0.00|
|Exports|4,425|5.32|3.54|5.22|4.22|
|Imports|-3,771|5.14|3.44|5.26|4.15|
|Real Income per capita|3,790|3.81|2.02|3.83|2.60|
||End-of-p<br>|eriod values<br>|(for the corr<br>|esponding p<br>|eriods)<br>|
|Real exchange rate|1.00|1.012|1.066|1.015|1.108|
|Welfare (EV)||||25|1,266|
|Trade-to-GDP|63.4|61.8|59.6|61.5|58.0|
|Food CPI|1.000|1.011|1.050|1.014|1.075|
|Non food CPI|1.000|1.011|1.062|1.013|1.099|

The evolution of factor markets matters not only for the external competitiveness of Panama but also for its pattern of sectoral specialization. Several major trends are observed in the BaU scenario and summarized in Table 4: acceleration of skilled wage

> 13 In the Canal scenario, the first period also corresponds to the construction phase and the second period to the operation phase.

> 14 The model assumes that all technological change is Harrod-neutral, i.e. labor-augmenting.

11
Figure 2: Satisfaction rate over time.

<!-- Start of picture text --> Jan11 May11 Aug11 May12 Aug12 Sep13<br>70<br>60<br>50<br>Percent<br>40<br>30<br><!-- End of picture text --> _Note: people answering either or ’very_ The waves considered by the study (January 2011, May 2011, August 2011, May 2012, August 2012, and September 2013) include a special module on the gas subsidy reform implemented in April 2011. The surveys were conducted through face-to-face interviews. Each survey between January 2011 and August 2012 include a total of 1,200 adult respondents drawn from a stratified random sample using the population census as frame. The wave of September 2013 polled 610 respondents. The samples were designed to be nationally representative with a margin of error of ±2.9 percent and a 95 percent confidence level.

The January 2011 survey, conducted prior to the implementation of the subsidy reform includes a more comprehensive set of questions, which allows exploring different dimensions associated with _ex-ante_ satisfaction with the reform. The survey included direct questions assessing individuals’ satisfaction with the reform (whether individual consider the reform a good or bad idea, what positive consequences they thought the reform would bring about, what negative consequences they thought the reform would bring about, whether they considered that the reform would have mainly positive or negative consequences for families like them); their level of information about it (how much they estimated the real cost of a 25-lbs bottle of LPG without subsidy to be, how informed they considered themselves to be about the reform, what they understood the subsidy reform to be); their trust in the government fulfilling its commitment (whether they thought that they would receive the compensation that the government had promised); and their political views (party for which they voted in the last election, whether their political views are aligned with government party or 12
<!-- Start of picture text --> Impact Evaluation Timeline<br>Jan March March 2010- Feb Sept May July 2011-June Feb July<br>2010 2010 2011: Round 1 2010 2011 2012: Round 2 2012 2012<br>&<br>a — 5<br>=gg<br>-&a5 a5<br>°ov<br>=£=<br>=5 £ 8 &<br>:oo E E<br><!-- End of picture text --> <!-- Start of picture text --> 2106<br>Originally recruited<br>25 2081<br>Pregnant (assigned to Entered<br>Round 2) randomization<br>1273 808<br>Assigned to Round 1 Assigned to Round 2<br>(Treatment Group)<br>118 1155 39 769<br>Never started Started training Re-assigned to Round Control Group<br>training 1<br>1191 36<br>Entered Round 1 Started training<br>1131<br>Completed Round 1<br><!-- End of picture text -->
close relationship between education level and income changes, the regression results in Table 5 show that increasing return to education and difference in returns to occupations are major forces that widen income inequality.

# **Table 5 – Estimation of income equation**

Dependant <u>variable: logarithm of real household per capita income</u>

||Ur<br>|ban<br>|Ru<br>|ral<br>|
|---|---|---|---|---|
||1989|2004|1989|2004|
|Proportion of male adults|0.133<br>(1.50)|0.173<br>(1.44)|0.201*<br>(1.78)|0.306***<br>(3.82)|
|Average age of adults|0.004**<br>(2.53)|0.024***<br>(10.29)|0.009***<br>(4.81)|0.011***<br>(5.66)|
|Average of completed years of formal education|0.031***<br>(5.19)|0.086***<br>(10.85)|0.046***<br>(6.77)|0.065***<br>(8.98)|
|Number of household members who have the following<br>professions (Ref.: army and police, other occupations or|||||
|out of employment)|||||
|Senior professional/technical personnel (doctor,<br>professor, lawyer, architect, engineer, etc.)|0.106***<br>(2.84)|0.550***<br>(6.48)|||
|Professional/technical personnel (midwife, nurse,<br>teacher, editor, photographer, etc.)|0.054*|0.458***|||
||(1.69)|(5.27)|||
|Administrator/executive/manager, factory manager,<br>government official, section|0.123***<br>(4.24)|0.415***<br>(5.66)|||
|Office staff (secretary, office helper, etc.)|0.076***<br>(2.61)|0.461***<br>(6.13)|||
|Farmer, fisherman, hunter, logger, etc.|-0.058***<br>(-2.90)|-0.182***<br>(-4.30)|||
|Technical, skilled worker (foreman, craftsman, etc.)|0.081***<br>(4.22)|0.320***<br>(4.90)|||
|Non-technical, non-skilled worker (laborer)|<br>0.037**<br>(2.38)|<br>0.276***<br>(4.27)|||
|Service worker|0.040**|0.358***|||
||(2.00)|(6.74)|||
|The number of household who had a secondary|||||
|occupation|0.032|0.205**|||
||(1.46)|(2.07)|||
|Number of household members participate in the|||||
|following types of firms (Ref.: collective)|||||
|State|||0.313***|0.562***|
||||(11.00)|(12.23)|
|Urban private|||0.070*|0.327***|
||||(1.87)|(10.72)|
|Household farming|||<br>-0.090***<br>(-7.13)|<br>-0.064***<br>(-2.92)|
|Others|||<br>-0.077***|<br>0.430***|
||||(-5.88)|(4.72)|
|Amount of household’s farmed land|||0.046**|0.068***|
||||(2.26)|(4.96)|

15
is under constant expansionary pressure both from demographic trends and, among the poorest countries, from low initial levels of educational coverage.

1.06 In view of these characteristics, education is particularly vulnerable to the effects of economic adjustment and public resource constraints. Increasingly, therefore, the desire to shelter the poor from the adverse social effects of adjustment has become an explicit rationale for including the social sectors in PERs.

B. Scope and Obiectives

1.07 Recent Bank reviews have covered PERs in general and the treatment of the main economic sectorsY Education sector issues in PERs, however, have not yet been evaluated. Specifically, this review has the following objectives: a) To assess the e: te-nt to which the increased attention to education in PERs contributes to a more integrated treatment of education sector issues within the overall economic environment, particularly in adjusting countries.

b) To compare the government's sector objectives, national expenditure priorities, and policy .eforms and, where they are inconsistent, to suggest improvements.

c) To review the diversity in methodological approach, data treatment, and sector coverage among education PERs, identifying best practices according to country-specific circumstances.

VA study of countries undertaking structural adjustment in the period 1979 to 1983 found that 68 percent effected reductions in education spending. In 22 percent of the cases, the education reduction was less in percentage terms than the aggregate reduction; however, in another 46 percent of the cases, education was among the most vulnerable sectors. A separate review of six low-income and lower-middle-income countries that underwent structural adjustment in the 1980s found that the decline in public expenditure on education as a percentage of GNP between 1980 and 1985 was 0.8 percent--almost twice the reduction experienced by low- and lower-middle-income countries in general. The outcome in the absence of managed adjustment, however, is unclear. Marlaine E. Lockheed and Adriaan M. Verspoor, Improving Primary Education in Developing Countries: A Review of Policy _Qptinsi,_ PHR (1990), p. 24 (draft).

2/Tariq Husain, "Public Expenditure Reviews: West African Experience 1982-86," World Bank, December 19, 1986; Tariq Husain, 'The Future Role of Public Expenditure Reviews in the Bank," (no date); Johannes Linn, "Lessons from the East Asia and Pacific Region: Experience with Public Expenditure and Investment Reviews," June 1986; Martha de Melo, "Public Investment/Expenditure Reviews: The Bank's Experience," Country Economics Department, June 1988 (draft); and Mohsen A. Fardi, "Generic Approaches to Expenditure Analysis in Sectoral Public Expenditure/Investment Reviews: The Bank's Best Practices," SPRPA, January 1989, (draft).

2
and then an Appeal to the High Court. In Thailand,<sup>appeals aremadetotheInformation</sup> Disclosure Tribunal, and in Hong Kong, China,<sup>totheOmbudsman.Itisclear thatseveral</sup> institutional features need to be developed to ensure<sup>thereis effectiveimplementationof FOIs.A</sup> survey by the Bulgarian Access to Information Programme<sup>Foundationin 2000,foundthat one</sup> year after the country adopted a freedom of information<sup>law,only: 42% of the Bulgarianpublic</sup> administration had implemented it effectively. A study<sup>bytheRomanianAcademicSociety</sup> showed that while "while 68 percent of Romanian<sup>institutionshadanofficeinchargeof</sup> informing citizens about what they do, only 16 percent<sup>had the requiredlist of data informing the</sup> public." (Reuters, Dec 17).16 Despite these caveats, however,<sup>it is possible to say that a country</sup> with an FOI law is more likely to be more open having<sup>takenan importantstep towardsallowing</sup> better information flows from the public sector to<sup>the private sector.</sup> Not only are FOI laws a relatively recent phenomenon<sup>on the scene(seeTable2)with</sup> only 50 countries having adopted one as of May 2002<sup>and 54 as of end 2002, but manycountries</sup> are still trying to work out how to implement them<sup>effectively.Preciselybecausetheadoption of</sup> such laws is relatively recent, it some countries it<sup>might be difficultto arguethat they have had a</sup> substantial effect on governance. Yet, even in these<sup>casesit mightbe argued that adoptionof a</sup> FOI act may be taken as one of the acts a government<sup>takesinan ongoingprocesstoimprove</sup> transparency: it is rarely the first act. Thus the existence<sup>of anactmay beanindicatorfora</sup> general move towards ensuring greater access to<sup>information.</sup> i-I B) Other Data I use two sets of governance/or institutional quality<sup>indicatorsto study the relationshipbetween</sup> transparency, and governance. The first set is developed<sup>by(Kaufmann,KraayandZoido-</sup> Lobodan, KKZ, 1999). The second set is composed of<sup>the ICRG indicators. The KKZ indicators</sup> 16 This is taken from an article by Antonia Oprita "Romania<sup>mustshape up lawsto get into EU'"</sup> 14
22 access, as their exports increased dramatically in this period.? Taking into account that MFA imports from these countries increased in 1990 by at least one third (see footnote 32), this expansion would not have occurred without increases in EC quotas.34

**Table** 7: The **MFA Group: Exporfl** to **the EC** and Pr-IrAs Market Access

||Exports<br>1991|Share in Indust<br>Exports, 1991|rial Index 1991<br>1988=100|NTB Coverage Simple Average<br>Ratio,<br>Tariff Rate|Range of<br>tariff rames<br>Max-Min|
|---|---|---|---|---|---|
||(in US|S million)||_(in_percent)||
|Bulgaria|142|20.9|227|90.6<br>10.8|17-0.0|
|FCSK|630|13.6|202|87.6<br>10.7|17-0.0|
|Hungary|712|21.4|175|85.1<br>0.1|9.3-0.0|
|Poland|1099|17.6|258|88.8<br>0.0|8.6-0.0|
|Romania|480|28.2|96|86.2<br>0.1|".3-0.0|

Sources: See Table 2.

On -the supply side, it is noteworthy that EC's expanding inports of textiles and clothing from the CEE-5 coincided also with the implaton of transformation-cm-stabilization programs, which provided a boost to MEA expo ts. For instance, the value of MFA imports from Bulgaria rose by 87 percent in 1992 (the first full year of the program in place), from the FCSK by 53 percent in 1991 (and 46 percent in 1992), from Hungary increased by 43 percent in 1990 (by 24 percent in 1992), and from Poland by 74 percent in 1990 (35 percent in 1992). Overall, between 1988 and 1992, the value of MFA imports into the EC from Bulgaria increased by 324 percent, from the FCSK by 194 percent, from Humgary by 116 percent, from Poland by 248 percent, and from Romania by 32 percent.' 33 It is apparent when comparing thetanmmal rates of growth of MFA imports into the EC in 1989 and 1990. The rate of growth of imports from Bulgana rose from 8 percent to 52 pemnt, fom the FCSK fram (-)1.2 to 33 percent, from Hungary hom 3.5to43 percen, and from Poland from4. ito 71 percent. The value of imports from Romania fell by 1.6 percent.

3 For instnce, in 1992 the aveage utilization ratio for Polish imports of textiles and clothing product into the EC was 33 percent (Dunin-Wasowicz, 1993:18).

3 As a result, its shr in their industrial imports into the EC increased especially in the case of Balkan countres where it almost doubled-for Bulgaria, the share rose friom 15 percent m 1988 to 29 percent in 1991 and for RomaWia from 20 persent to 39 percent (this was the only group of industrial products discussed here that recorded an increase in 1992);: