rafmacalaba/datause-encoder-data huggingface dataset:
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Rail

## **Achievements**

Zambia’s rail sector is critical to its mineral-based economy. Rail transport continues to be the most competitive for large bulk, time-insensitive commodities, such as Zambia’s copper production. The country’s rail network has two operators: (i) the Railway Systems of Zambia (RSZ) serves the north-south corridor and connects with the Zimbabwean rail operator for onward service to the Port of Durban; (ii) the Tanzania and Zambia Railway Authority (TAZARA) operates an eastward route from the copper belt into Tanzania and on to Dar es Salaam. The first of these is an awarded concession, while the second continues to be operated directly by the state.

**Table** **<u>3. Railway indicators for Zambia and select countries, 2000–05</u>**

||CFM (Angola)|BRC (Botswana)|CEAR (Malawi)|RSZ (Zambia)|Spoornet (South Africa)|TAZARA (Tanzania-<br>Zambia)|TransNamib (Namibia)|NRZ (Zimbabwe)|
|---|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state run (0)|0|0|1|1|0|0|0|0|
|Traffic density, freight, 1,000 tonne-<br>km/km|469.0|827.0|90.1|406.1|2,426.9|461.3|475.3|901.8|
|Passenger density, 1,000 passenger-<br>kms/km|||38.0|91.9|60.3|147.0|32.7|165.6|
|**Efficiency**|||||||||
|Labor productivity (1,000 traffic units per<br>employee)|121.0|722.1||502.0|3,308.1||||
|Carriage productivity (1,000 passenger-<br>km per carriage)|||1,176.5|3,285.7|||||
|Locomotive productivity (million traffic<br>units per locomotive)||||25.1|||||
|Wagon productivity (1,000 net tonne-km<br>per wagon)||||376.5|||||
|**Tariffs**|||||||||
|Average unit tariff (UT), freight, U.S.<br>cents/tonne-km|3.0||5.8|3.9|||||
|Average UT, passenger, U.S.<br>cents/passenger-km|1.0||1.0|0.8|||||

_Source:_ Bullock 2009, derived from AICD railways database downloadable at http://www.infrastructureafrica.org/aicd/tools/data Empty cells denote that data not available.

## **Challenges**

Zambian railways’ low traffic densities are well below the viability threshold of at least 2 million tons per kilometer for railways of this kind, making it difficult to capture the revenues needed to maintain assets. Also, performance for the RSZ is mixed, while TAZARA performance data is largely unavailable (table 3).

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**Table A6: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Theil Index)**

||**NCR Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>**A**|**Visayas **<br>**ll Men**|**Mindanao**|**NCR **|**Luzon **<br>**All**|**Visayas **<br>**Women**|**Mindanao**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Theil index|0.18<br>0.21|0.29|0.27|0.15|0.19|0.24|0.24|0.23|0.26|0.35|0.32|
|**Residual**|**0.20**|**0.35**|**0.29**||**0.05**|**0.04**|**0.04**||**0.18**|**-0.23**|**-0.54**|
|**Personal Attributes**|**-0.02**|**0.26**|**0.39**||**0.15**|**0.41**|**0.60**||**0.05**|**0.16**|**0.67**|
|_Gender_|-0.09|-0.02|-0.04||--|--|--||--|--|--|
|_Experience_|0.03|0.03|0.10||0.10|0.11|0.15||-0.05|0.01|0.24|
|_Years of completed schooling_|0.04|0.24|0.33||0.05|0.29|0.45||0.10|0.14|0.43|
|**Sector**|**0.43**|**0.10**|**0.04**||**0.73**|**0.52**|**0.34**||**0.03**|**-0.03**|**-0.17**|
|**Occupation**|**0.38**|**0.28**|**0.27**||**0.06**|**0.03**|**0.02**||**0.74**|**1.04**|**0.95**|
|**Year**|**0.01**|**0.01**|**0.01**||**0.00**|**0.00**|**0.00**||**0.00**|**0.07**|**0.09**|

20
following an approach similar to that suggested by Dang and Lanjouw (2013). However, this would move us away from the household-level analysis of the non-parametric approach to the agecohort analysis performed under the parametric method. To avoid this, I test the sensitivity of the joint probabilities to changes in the value of the γ weights in Figures 2 and 3. In general, the results are fairly stable to the value of the weights; except for extreme values, most estimates fall within the 95 percent confidence intervals.

Figure 4 performs the same sensitivity analysis to the conditional probabilities shown in Table 4. The method works well for γ = 0.5; most of the estimates fall within the 95 percent confidence interval when taking an unweighted average of residuals. However, since both numerator and denominators are estimated in this figure, results are less accurate than those shown in Figures 2 and 3 (Dang and Lanjouw 2013) and extreme values fall outside the 95 percent confidence interval. Thus, caution should be exercised when selecting the weights to estimate conditional probabilities of moving in and out of poverty given the initial poverty status.

# _4.3. Subgroup and household-level poverty mobility_

This section tests the performance of the methodology in measuring poverty dynamics for specific subgroups of the population. To do so, we estimate the same poverty transitions for several subgroups based on: region of residence, gender, age, and education of the household head. Subgroup synthetic panel results are based on parameters estimated from the entire sample. All estimations are based on specification 3 and one repetition is considered ( _R_ = 1).

Figures 5 and 6 compare actual mobility from panel datasets with estimations based on the synthetic panel using the third specification in Tables 2 and 3. All estimates are close to the 45degree line, suggesting that the technique works well for estimating poverty dynamics profiles based on subgroups of the overall population independently of the panel length.

Figure 7 validates the methodology for estimating non-anonymous growth incidence curves (GIC). These curves show household-level welfare growth by deciles of the first-round welfare distribution.<sup>9</sup> All estimations are based on specification 3 and are compared with the actual non-anonymous GIC from panel data. All estimated GICs from synthetic panels are close to the actual income growth. These results are extremely relevant since, unlike the parametric approach

> 9 As opposed to anonymous GICs which show decile-level (or any other percentile) welfare growth by deciles (or any other percentile) of the first-round welfare distribution.

11
Table 1 Summary Statistics and Balance

|||_Trea_|_tment_||
|---|---|---|---|---|
||Control|Payroll<br>- control|Account<br>- control|Payroll<br>- account|
||(1)|(2)|(3)|(4)|
|||**Panel A: d**|**emographic**|**s**|
|Female|0.576<br>|0.011<br>|0.005<br>|0.006<br>|
||(0.019)|(0.023)|(0.028)|(0.024)|
|Married|0.720|-0.003|-0.004|0.001|
||(0.018)|(0.021)|(0.026)|(0.022)|
|Dependent children|0.565|0.018|0.014|0.004|
||(0.019)|(0.023)|(0.028)|(0.024)|
|Education|3.289|0.066|0.217|-0.151|
||(0.059)|(0.070)|(0.091)|(0.078)|
|Literate|0.573|0.028|0.067|-0.039|
||(0.019)|(0.023)|(0.028)|(0.023)|
|Digit span score|5.143|-0.050|0.029|-0.078|
||(0.048)|(0.057)|(0.068)|(0.057)|
|Monthly wage, taka|6778.743|139.119|65.944|73.174|
||(59.029)|(73.771)|(94.019)|(85.515)|
|Work experience, years|3.840|-0.078|-0.394|0.316|
||(0.166)|(0.183)|(0.182)|(0.108)|
|Experience in current job, years|3.055|-0.056|-0.420|0.364|
||(0.077)|(0.090)|(0.104)|(0.085)|
|Expected tenure in current job, years|4.364|-0.375|-0.967|0.592|
||(0.475)|(0.576)|(0.654)|(0.555)|
||**Panel B: f**|**nancial ex**|**perience an**|**d planning**|
|Ever used bank account|0.054|-0.008|0.006|-0.014|
||(0.009)|(0.010)|(0.013)|(0.011)|
|Ever used mobile money|0.770|-0.002|0.006|-0.008|
||(0.017)|(0.020)|(0.024)|(0.020)|
|Ever received mobile money|0.206|-0.002|0.049|-0.051|
||(0.016)|(0.019)|(0.024)|(0.021)|
|Has balance in bank account|0.054|-0.008|0.006|-0.014|
||(0.009)|(0.010)|(0.013)|(0.011)|
|Has balance in mobile money account|0.003|0.001|0.009|-0.008|
||(0.002)|(0.003)|(0.005)|(0.005)|
|Basic expenditures control|0.469|0.013|-0.012|0.025|
||(0.020)|(0.023)|(0.028)|(0.024)|
|Income sharing|0.462|0.009|-0.045|0.054|
||(0020)|(0023)|(0028)|(0024)|
||.<br>**P**|.<br>**anel C: sav**|.<br>**ings and d**|.<br>**ebt**|
|Has savings|0.498|0.008|0.017|-0.009|
||(0.020)|(0.023)|(0.028)|(0.024)|
|Has formal savings|0.283|-0.031|-0.050|0.019|

40
from neighboring districts in other states. Similar patterns are observed when we look at out-migration from Nagpur to other districts in Figures 3c and 3d. The most popular destinations of Nagpur’s emigrants are neighboring districts in Maharashtra (Bhandara, Wardha, Amravati, and Chandrapur) which receive a total of 32 percent of emigrants from Nagpur. Neighboring districts in other states receive much fewer migrants when compared to distant coastal districts of Maharashtra (see Figure 3d).

Table 4 presents summary statistics by gender and various other dimensions. The first disaggregation is by age groups. The share of migrants are highest among those between 25 and 65 years of age. The gap is especially stark for women where the migrant ratio dramatically increases from 23.2 percent for 14-19 year olds to 69.1 percent for the 25-34 year olds, highlighting the role of marriage in migration. The corresponding increase is less drastic among men.

The second set of rows in Table 4 illustrates patterns of migration for four education levels: (i) illiterate, (ii) primary school education, (iii) secondary school education, and (iv) tertiary education. People with higher educational levels appear to be more mobile. This holds for both aggregate levels of migration as well as for movements across geographical boundaries. For instance, migrants account for 35.6 percent of the tertiary educated male population compared with 11.5 percent for the illiterate males, and inter-state migrants represent 8.4 percent among the former but only 2.1 among the latter. The patterns are similar for females.

The reason for migration (third set of rows in Table 4) is one of the most important questions in the census. We aggregated the answers into five main categories: (i) work or business, (ii) marriage, (iii) move with the family, (iv) education, and (v) other reasons. For men, work/business, move with the family and others are the main reasons (around 30 percent each) while marriage dominates all other categories for women (70 percent). Unfortunately, the format of the data does not allow us to construct cross-tabulations, such as by education and reason for migration, which would provide further insights.

Closely linked to the propensity of moving across geographical boundaries is the 10
health care and health litigation. Section III develops a simple model of the political economy of health care- the aim of the section being to generate a set of hypotheses, whose robustness can be tested. Subsequently, Section IV, using a unique dataset of stakeholder perceptions from Uruguay, empirically evaluates the veracity of these theoretically derived hypotheses. The final section concludes with a critical evaluation of the findings and implications of the previous sections.

# **II. Literature Review**

Virtually all developed and many developing countries provide some form of publicly financed health services to their citizens. Given the scarcity of resources, governments have had to devise mechanisms to determine the nature of health service allocation. The ensuing political economy of health care allocation is thus, characterized by potentially complex considerations of both: (1) _efficiency_<sup>_5_</sup> - are publicly financed health services efficiently/poorly allocated? and (2) _equity_<sup>_6_</sup> - what are the implications of providing different levels of heath care services to (sub-sets of) the population? Unsurprisingly, all elements of this process are potentially subject to classical principal-agent problems as the interests of the agents (health bureaucracies and providers) may deviate from those of their principal (health care consumers). The magnitude and nature of any such principal-agent dynamics varies depending on how the institutional context conditions the nature of interaction between the two. Specifically, a wide range of institutionally induced incentive structures have been developed/used over time, to try and mitigate conflicts of interest between public health care providers (agents) and (potentially multiple) consumers (principals) (Besley, 2006).<sup>7</sup> One such institutional mechanism that can alter the dynamics of interaction, between health care providers and consumers, is health litigation. That is the use of court rulings to change decisions regarding the allocation (efficiency) and access (equity) of the public health care system.<sup>8</sup>

> 5 Formally efficiency can be defined as the extent to which input resources, of a health care system, are transformed into intended outcomes (consumer demands are satisfied) as opposed to diverted to other activities (the private agenda of health care providers).

> 6 Formally equity can be defined as equal access to health care goods and services regardless of individual identities, and resource endowments.

> 7 Besley, T. Principled Agents?: The Political Economy of Good Government. Oxford University Press, USA, 2006. 8 Gloppen, Siri. "Litigation as a Strategy to Hold Governments Accountable for Implementing the Right to Health." Health and <u>Human Rights. 10.2 (2008): 21-36.</u>

3
denote the set by I. The object of study is M, the set of two-stage measures with components and . Which axioms are satisfied by these measures? Can we identify a MM: XX→RR subclass of M that is both intuitive and axiomatically sound? h εε H II εε I The properties defining H and I ensure that every measure in M satisfies the axioms of anonymity, scale invariance, and replication invariance.<sup>26</sup> Our first result takes up the weak uniform majorization axiom.

uniform majorization axiom. **Theorem 1** . Let be a two-stage measure with components and . If satisfies weak uniform majorization, then there exists 1 ) ≫0 such that 1 1 MM h εε H II εε I MM for all . nn nn nn cc= (cc , ... , cc h(vv) = cc vv + ⋯+ cc vv **Proof** . See the Appendix. vv εε VV<sup>⋯</sup> The proof draws on Dardanoni (1995) and begins by showing that any convex combination of allocations in having the same value under h also has the same value under h. Applying this to a certain set of allocations yields a simplex over which h is linear, while the remaining VV<sup>⋯</sup> argument expands the characterization to all of . Theorem 1 identifies the two-stage measures that are consistent with weak uniform majorization; the remaining measures in M VV<sup>⋯</sup> violate this basic axiom and hence are not axiomatically sound.<sup>27</sup> violate this basic axiom and hence are not axiomatically sound.<sup>27</sup> Let L be the subclass of M whose aggregation functions are linear with . The next result describes the axioms satisfied by the measures in L. L. . cc≫0 describes the axioms satisfied by the measures in L. L. . **Theorem 2.** Any two-stage measure satisfies the anonymity, scale invariance, replication invariance, limited uniform majorization, and unfair rearrangement axioms. MM εε L

**Proof** . See the Appendix. The proof shows how each invariance property for follows immediately from the analogous property for . For the limited uniform majorization axiom, when a bistochastic matrix is applied MM to array , the new aggregate vector can be found by applying the same bistochastic matrix to the II xx

> 26 See Theorem 2, below.

> 27 This includes two-stage measures using other CES-type aggregation functions suggested by Maasoumi (1986) and used in empirical applications.

12
# Table 3: Round 1 characteristics - Government officials survey

||(1)<br>ULGSP LGA|(2)<br>Control LGA|(3)<br>Dif|(4)<br>p-value|(5)<br>N|
|---|---|---|---|---|---|
|Master plan up to date|0.46|0.27|0.19|0.03|120|
|Master plan approved in previous 2 years|0.33|0.12|0.21|0.00|120|
|LGA has formal plan for capacity building|0.94|0.62|0.33|0.02|39|
|Showed formal plan|0.76|0.46|0.30|0.09|30|
|Percentage of unplanned settlements|48.96|48.45|0.51|0.94|59|
|Seldom experience delay in preparing fnal budget|0.86|0.86|-0.01|0.92|155|
|Seldom experience delay in guidelines from central gov. for fnal budget|0.53|0.56|-0.03|0.71|156|
|Agrees that budget was executed in accordance with expected results|0.16|0.11|0.05|0.14|464|
|Agrees that disbursements from central government were timely|0.09|0.04|0.05|0.02|453|
|Position flled? Municipal/City/District Internal Auditor|0.94|0.79|0.16|0.01|120|
|Agrees that ofce of internal audit is independent|0.68|0.58|0.10|0.02|459|
|Agrees that internal audit is efective in monitoring|0.85|0.70|0.15|0.00|467|
|Number of engineers is adequate|0.19|0.18|0.01|0.89|80|
|Agrees that payments to suppliers were carried out on time|0.16|0.12|0.04|0.17|456|
|Number of meetings discussed infrastructure|2.82|2.98|-0.16|0.71|352|
|Ofcial system to handle grievances|0.88|0.79|0.09|0.01|474|
|Have handled grievances personally|0.79|0.76|0.03|0.44|474|
|Number of grievances (year)|329.08|398.52|-69.44|0.45|366|
|Grievances handled through formal system|0.92|0.92|0.01|0.76|311|

_Note:_ This table presents average traits of government officials in ULGSP and control LGAs, and the difference between the means. P-value is reported for difference of means, clustered at LGA-level.

45
5 <!-- Start of picture text --> dXA GB<br>T.B dXB GA<br><!-- End of picture text --> where Gi - 8G/8Xi > 0, i - A,B, iadizates pos tive marginal costs, and the bowed out transformation curve reflects the fact that marginal costs increase with output. 3/ The equilibrium for the competitive industry requires that and Pi - O*Gi where G* is the marginal cost of producing a unit of X. In figure 1. the equilibrium condition (1) is represented by the tangency of the export transformation curve and the price line P*, at the unrestricted equilibrium, A*.

Now impose a VER which restricts exports to A to the level XA. The restricted allocation is represented by the new price-quantity pairs (PA, XA) and (PB, XB) where superscript bars indicate the restricted equilibrium. Because we have assumed that the industry as a whole does not behave like a discriminating monopolist (due to the assumption of an atomistic industry), it is possible that the VER will raise revenues. Denoting by LA, **6,** '< 0 the elasticities of demand in the restricted and unrestricted markets respectively, a departure from the free trade equilibrium will raise revenues if marginal sales revenue is higher in B than in A, i.e. if PB(1- l1/B) > PA(l - VLWA). Thus if the elasticity of demand is much greater in the unrestricted than in the restricted market, a
**Table 10.  Consolidated Financial Statements of KKVS CDAs**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|**Income**||||
|Interest on loans|98,210<br>|390,529<br>|1,038,411|
|Service charges/Fees|110,189|152,183<br>|372,639|
|Donations|25,800|15,779<br>|24,643|
|Sundry Income|30,946|98,809<br>|41,722|
|Total|265,145|657,300<br>|1,477,415|
|**Expenditure**||||
|Interest|22,720|244,314<br>|969,917|
|Salary and benefits|80,870|140,830<br>|211,566|
|Travel|1,849|32,506<br>|48,099|
|Office administration|37,173|13,778<br>|17,523|
|Printing and Stationery|12,570|21,284<br>|24,413|
|Training|4,764|32,713<br>|43,924|
|Audit fee|2,231|5,800<br>|8,000|
|Sundry expenses|27,573|85,575<br>|32,412|
|Profits|75,395|80,500<br>|121,561|
|Total|265,145|657,300<br>|1,477,415|
|**Equity and Liabilities**||||
|Reserves and Surplus|-<br>|704,264<br>|733,224|
|Member Deposits|-|170,803<br>|318,202|
|Loans|-|3,329,545<br>|8,511,827|
|Specified Funds|-|812,113<br>|1,169,766|
|Current liabilities|-|151,930<br>|153,017|
|Total|-|5,168,655<br>|10,886,035|
|**Assets**||||
|Land and Fixtures|-|62,600<br>|97,996|
|Deposits|-|424,300<br>|525,506|
|Loans and Advances|-|4,333,581<br>|9,521,502|
|Cash|-|348,174<br>|741,031|
|Total|-|5,168,655<br>|10,886,035|

Item heads in original statements reorganized and relabeled for easier comparison.

- 32 -
3 are based on the Population Censuses. Year to year changes in employment monitored collected on an annual basis in the SAKERNAS show quite large fluctuations: they are only useful for examining medium to longer term trends in employment.

The paper starts with an examination of employment and wage growth on a national scale and on Java (compared with the Outer - Islands as a group) over the period 1987-1994. This is followed by an examination of employment and wage change by major sector in various regions of Indonesia: we first deal with longer term trends, and then examine more recent labour market developments. A final section examines inter-regional wage structure.

# **II.** CHANGING NATIONAL ECONOMIC STRUCTURE AND LABOUR MARKETS

Economic growth accelerated in the second half of the 1980s. Indonesia regained overall rates of growth of close to seven per cent, similar to those achieved in the oil boom period of the 1970s (Table 1). Growth rates increased from just over five per cent per annum in the period 1983-1987 to just under seven per cent in 1987-1993, and were slightly higher in the following year.5 Three points stand out regarding the patterns of growth from 1987 onwards.

(i) Despite the jump in non-oil exports, it was not manufacturing which accounted for the surge in economic growth rates. Although manufacturing growth rates were still very high - around 12 per cent per annum for non-oil manufacturing - they were not significantly different from those achieved in the early 1980s. Rather it was the mainly expansion in the non-traded goods sectors which accelerated.

(ii) Government administration grew much less rapidly than in previous periods. This was in contrast to the oil boom and slower growth periods when government administration grew rapidly and made a major contribution to overall growth rates (Sundrum, 1986, 1988).

(iii) The agricultural sector continued to grow steadily at just over three per cent, although its share in total GDP declined more rapidly than in the period of slower economic growth in the early 1980s.

5 The series was rebased in 1993 and expressed in 1993 prices. The exercise suggests that the contrast in growth rates between may have been even greater the two periods than the above figures imply.
exporters to all countries except China (or India) we add these four variables (exports of China to the third market, imports of China from the third market, exports of China to LAC and imports of China from LAC) to the specification of model (1).

To account for potential differences in the relevant elasticities across the LAC subregions, we also include the products of these four variables with dummy variables that take a value of 1 when region _R_ is an exporter.<sup>8</sup> The final specification for China, for example, is given by: This same specification applies to the estimation of the relevant elasticities for the case of India.

# **2. Results**

The following paragraphs discuss the econometric estimates of the relevant demand and supply elasticities of model (2) and of the complementarity or substitution elasticities in model (3). The discussion focuses first on the effect that the growth of China and India’s demand (as well as LAC’s GDP growth) may have had on exports of LAC to these Asian economies, as in model (2), using data on aggregate non-fuel merchandise exports. We then turn to the impact of China and India’s trade flows on LAC exports to (and imports from) third markets through the four channels indicated in equation (3).<sup>9</sup> For ease of exposition, we do not report or discuss the resulting estimates of the other explanatory variables, but our estimates of the standard gravity-model variable coefficients have the expected signs and all are significant, except for the Linder variable capturing the similarity in GDP per capita between LAC economies and their trading partners, which is

> 8 As in the estimation described in section 2.1, we also allow for heterogeneity across regions on the import side, but we do not include them in equation (3) below for ease of exposition.

> 9 The appendix presents the results of the impact of China, India, and LAC GDP growth on LAC imports from these two Asian economies.

9