rafmacalaba/datause-encoder-data huggingface dataset:
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA evident in Oshakati in Namibia, where the commercial consumers pay about 20 times more than the residential customers for 100 m<sup>3</sup> of water (figure 13).

Not only the levels, but the rate of increase in nonresidential tariffs is higher. For instance, in Niger, the standpost and low-volume tariffs have barely increased since 2000, but the industrial and commercial tariffs have registered a 6–7 percent growth in nominal terms (figure 14). While the Niger case is one of differential price increase, the LWB case is of equal price rise. The increase across all tariff bands has been equal in LWB in Malawi, though the increase at each level has been different. What emerges from this analysis is the discrepancy of prices among residential and nonresidential consumers. Infrastructure price inefficiencies create distortions in the development of the dynamic private sector as well.

## **Figure 14. Evolution of tariffs in Niger and Malawi**

SPEN (Niger), 2000–06 <!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br>2000 2001 2002 200 3 2004 2005 2006<br>Government Commerce<br>Industry Public Fountain<br>Residential 0-15m3 Residential 11-40m3<br>Residential 41-75m3 Residential >75m3<br>LCU<br><!-- End of picture text --> LWB (Malawi), 1999–2006 <!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>Industry 0-100m3 Industry More than 100m3<br>Institutional Residential 0 - 10m3<br>Residential 10 - 40m3 Residential > 40m3<br>LCU<br>Sep-99 Sep-00 Sep-01 Sep-02 Sep-03 Sep-04 Sep-05 Sep-06<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

# Cross-subsidies between public standposts and residential consumers

Utilities also supply water to standposts in addition to piped connections to houses and yards. In recent years, the standposts have emerged as an important source of water supply to households, particularly in the periurban areas in Africa. To reach this segment of the population, prices at the standposts are usually subsidized so that low-income households can benefit from improved water supply. The important policy question is whether the objective of providing affordable water to standpost users is realized. Evidence suggests that the retail price charged by standpost or kiosk operators can be several orders of magnitude higher than the formal price, defeating the purpose of providing cheap water. This price may also be higher than the prices paid by low-volume consumers of piped water. From the AICD WSS Survey 2007, it is possible to compare the formal and informal standpost price with the low-volume piped consumer price for 12 utilities. This highlights the extent of cross-subsidy that exists between the low-volume consumers at standposts and piped connections (figure 15).

22
**<u>Appendix Table 2:  Labor allocation for fuelwood collection by men 10 years and older</u>**

|Male|(1)Collect|(2)Hours|(3)Collect|(4)Hours|(5)Collect|(6)Hours|
|---|---|---|---|---|---|---|
||Ov|erall|Less|Biomass|More|Biomass|
|Age in years|0.002|0.053*|-0.003|0.079**|0.013|0.005|
||(0.009)|(0.029)|(0.010)|(0.039)|(0.015)|(0.037)|
|Age Square|0.0000|-0.0006*|0.0001|-0.0009**|-0.0001|0.0000|
||(0.0001)|(0.0003)|(0.0001)|(0.0004)|(0.0002)|(0.0004)|
|Head Education: Snr. Prim or more|-0.092**|-0.151|-0.054|-0.028|-0.169**|-0.419*|
||(0.045)|(0.161)|(0.054)|(0.205)|(0.083)|(0.232)|
|HH Size|-0.040***|-0.051|-0.036***|-0.048|-0.052***|-0.055|
||(0.009)|(0.041)|(0.011)|(0.055)|(0.017)|(0.052)|
|Real Value of Assets '000|-0.002|-0.003|-0.002|0.000|-0.006|-0.015|
||(0.001)|(0.006)|(0.001)|(0.007)|(0.005)|(0.016)|
|Wet Season & Wet Area Interaction|0.009|0.123|-0.053|-0.032|0.148**|0.336**|
||(0.033)|(0.099)|(0.043)|(0.177)|(0.058)|(0.149)|
|Dry Season and Dimba Area Interaction|0.144|-0.121|0.127|-0.163|0.207|-0.334|
||(0.093)|(0.269)|(0.105)|(0.311)|(0.205)|(0.616)|
|Total hectares of land uncultivated|0.038|0.207|0.050|0.190|0.011|0.361*|
||(0.038)|(0.131)|(0.051)|(0.166)|(0.067)|(0.194)|
|Total hectares of trees|0.081|0.364|0.439|1.040|-0.965|-3.707|
||(0.408)|(1.471)|(0.468)|(1.816)|(0.951)|(2.563)|
|Predicted Real Wage for Women|-0.001|-0.002|0.000|-0.002|0.000|-0.001|
||(0.001)|(0.002)|(0.001)|(0.003)|(0.001)|(0.002)|
|Number of <5yr children|0.077|-0.009|0.131|0.048|-0.122|-0.634|
||(0.118)|(0.420)|(0.131)|(0.517)|(0.276)|(0.707)|
|Married|-0.468***|-0.368|-0.467***|-0.243|-0.482***|-0.613|
||(0.061)|(0.365)|(0.073)|(0.496)|(0.110)|(0.420)|
|Rural Centre|0.475***|0.557|0.441***|0.552|0.538***|0.821|
||(0.098)|(0.494)|(0.138)|(0.752)|(0.152)|(0.520)|
|Rural South|0.536***|0.832|0.535***|0.611|0.524***|1.450***|
||(0.098)|(0.524)|(0.142)|(0.810)|(0.145)|(0.513)|
|Wet Season Dummy|0.027|-0.096|0.053|0.051|-0.043|-0.144|
||(0.055)|(0.177)|(0.068)|(0.262)|(0.099)|(0.225)|
|ADMARC Market Distance KM|0.0008***|0.0046***|0.0009***|0.0042***|-0.0021|0.0047|
||(0.0003)|(0.0010)|(0.0003)|(0.0012)|(0.0030)|(0.0077)|
|Distance to Asphalt Road KM|-0.0005|-0.0052|0.0006|-0.0115*|-0.0020|-0.0026|
||(0.0009)|(0.0039)|(0.0011)|(0.0065)|(0.0014)|(0.0040)|
|Elevation|0.00010|0.00016|0.00015|-0.00002|0.00005|0.00050|
||(0.00008)|(0.00030)|(0.00009)|(0.00041)|(0.00014)|(0.00037)|
|Biomass20 (m3/ha)|-0.001|-0.003|0.001|-0.015|0.001|0.000|
||(0.001)|(0.003)|(0.004)|(0.017)|(0.002)|(0.004)|
|Price of Maize Grain MK/Kg|-0.012|-0.007|-0.016|-0.001|-0.015|-0.030|
||(0.010)|(0.036)|(0.013)|(0.051)|(0.020)|(0.047)|
|Recent illness/injury|0.113**|0.050|0.078|-0.116|0.180**|0.401*|
||(0.047)|(0.179)|(0.058)|(0.233)|(0.083)|(0.234)|
|Chronic illness|0.030|-0.078|0.122|-0.013|-0.199|-0.264|
||(0.064)|(0.208)|(0.075)|(0.291)|(0.127)|(0.317)|
|Other Members Chronically Ill|0.048*|-0.061|0.055*|-0.136|0.041|0.041|
||(0.026)|(0.092)|(0.032)|(0.132)|(0.047)|(0.110)|

48
comparing the GII‐303 level with the GII‐45 level, and the GII303 level with the GII‐12 level of disaggregation, we never fail to reject the null hypothesis that falling in the top 25 percent of explanatory variables at the highest level of disaggregation is statistically independent of falling in the top 25 percent at lower levels of disaggregation. This suggests that there is little evidence that variables that are identified as important at one level of disaggregation will be identified as important determinants of outcomes at another level of disaggregation.

Tables 5A and 5B report the results we obtain from applying the BMA methodology to the Doing Business dataset. The results are qualitatively very similar to those we obtain for GII. As with GII, we find that posterior probabilities are fairly strongly concentrated on a relatively small number of models, and a fairly small set of variables is identified as having large posterior inclusion probabilities. So as not to overinterpret the precise magnitudes of these probabilities, we again highlight the top 25 percent of explanatory variables according to their posterior inclusion probabilities.

Most interestingly for our purposes, we see that our main findings on stability across outcomes, and stability across levels of disaggregation, hold in this dataset as well. In Table 5A, at the least‐ disaggregated DB‐10 level, we find a good deal of stability across outcomes. Two out of 10 DB variables have consistently high PIPs across the seven outcome variables (the CrossingBorders variable which is in the top three PIPs for all seven outcome variables, and the GettingCredit variable, which is in the top three PIPS for five of seven outcomes). At the other extreme, 6 out of 10 variables appear in the list of top three regressors for just 0 or 1 outcomes, suggesting a great deal of stability across outcomes in terms of variables that appear to matter much for outcomes. More formally, when we test the null hypothesis that appearing in the top‐three list is independent across outcomes we very strongly reject the null of independence. This very attractive feature of the results breaks down however when we move to the DB‐41 level of disaggregation. Here for example we find that just 3 out of 41 indicators appear in the top 25 percent of regressors for 5 or more outcomes, and the chi‐squared test of the null hypothesis of independence across outcomes delivers a p‐value of 0.99.

Looking across levels of aggregation in the DB dataset, we also find the same feature of instability that we saw in the GII dataset. In particular, an individual indicator at the DB‐41 level of disaggregation is not more likely to be included in the top 25 percent of explanatory variables if the higher‐level aggregate to which it belongs is in the top 25 percent of variables at that level of disaggregation. To make this concrete, consider for example the DB‐10 indicator “GettingCredit” which 23
DTS continues, it will be important to continue to actively monitor the volume of all procedures separately to ensure that hospitals are accurately reporting their cases. Only with available and transparent data can accountability increase and improvements be made. For example, it would be useful to undertake a similar study in future years to examine imapcts on efficiency, volume, and quality.

Particular attention needs to be given to pneumonia cases, for which there has been a remarkable one year increase in cases from 2008 to 2009. Although the number of pneumonia cases seems to be back to 2005-levels, pneumonia had the highest increase in price under the DTS system—287% from the case-based price under PPTP in 2008 to DTS in 2009. This substantial increase likely provides a strong incentive for hospitals to code cases as pneumonia, and evidence from the United States and Bulgaria suggest that pneumonia codes are prone to upcoding and gaming (Silverman and Skinner 2004, Thomson 2006). It will be important for HZZO and Croatia to continue to monitor pneumonia cases to prevent any future upcoding or gaming.

# **References**

Cutler, D.M., 1990. Empirical Evidence on Hospital Delivery under Prospective Payment. Unpublished paper.

Cutler, D.M., 1995. The incidence of adverse medical outcomes under prospective payment. Econometrica 63 (1), 29–50.

- Dafny, L., 2005. How do hospitals respond to price changes? American Economic Review 95 (5), 1525–1547.

- Feinglass, J., & Holloway, J. J., 1991. The initial impact of the Medicare PPS on US health care: A review of the literature. Medical Care Review, 48, 91–115.

- HZJZ, Croatian Health Service Yearbook 2000, Zagreb: Croatian National Institute for Public Health, October 2001.

- HZJZ, Croatian Health Service Yearbook 2001, Zagreb: Croatian National Institute for Public Health, November 2002.

- HZJZ, Croatian Health Service Yearbook 2002, Zagreb: Croatian National Institute for Public Health, November 2003.

- HZJZ, Croatian Health Service Yearbook 2003, Zagreb: Croatian National Institute for Public Health, November 2004.

- HZJZ, Croatian Health Service Yearbook 2004, Zagreb: Croatian National Institute for Public Health, November 2005.

11
very rich and poor provinces is high, with recent estimates suggesting that between 1990 and 2003, the ratio of the per capita GDP between the richest and poorest province rose from 7 to 13 (Lou and Wang, 2007). Part of this can be explained by trade dynamics and by the fact that China is one of the largest powers in the world with only one coast. Differences in per capita budget revenues are quite large across provinces in China. Furthermore, the per capita spending differentials were high with the rich ones exceeding the poor ones by more than 8 times. Moreover, the Gini coefficient increased from more than 0.25 in the 1980s to more than 0.4 in 2007.<sup>2</sup> As in Africa, poor provinces in China are generally inland and are sparsely populated, while richer provinces are concentrated in the coastal areas. As China grows, its internal inequality is increasing while it is also affecting global prices.

In an increasingly interlinked world economy, the ascent of China is having ripple effects on the world economy, impacting other countries, including many resource-rich ones in SubSaharan Africa. As the country has grown in output, its external economic relations have multiplied. Zafar (2007) traces the growing role of China in Africa in terms of macroeconomics, trade, investment, and aid links and finds that China has exerted a generally positive influence on African terms of trade, growth rates, and exports, although the effect has been felt asymmetrically on the continent depending on country resource endowment.3 The empirical research also shows that much of the trade follows a standard Heckscher-Ohlin framework in terms of the commodity composition of that trade. In many ways, China is acting as Africa’s growth engine by influencing global commodity prices and by providing vital capital and investment and a slowdown in China translates into a greater slowdown in SSA. Trade between the two has grown exponentially reaching close to $ 60 billion by 2008, and Chinese investment and aid are

> 2 The evidence from China based on household survey data suggests that the Gini coefficient has now surpassed the US and is moving towards levels in Latin America. However, it is important to note that the Gini coefficient of land distribution in rural China was 0.49 in 2002 compared to 0.74 in 2003 for India. In Africa, the minimum overall Gini in 1998 was Madagascar with 0.38 and the highest was Lesotho with

> 0.66. Comparable rural Ginis are difficult to obtain due to methodological reasons.

> 3 Countries relative factor endowments determine the effect of Chinese shocks on global terms of trade. Oil and metal producers gain the most from China while oil-importing textile producers gain the least.

9
_-_ Trujillo, L. and Nombela, G. _Privatization and regulation of the seaport industry_

# LIST OF CONTENTS

|LIST OF BOXES/FIGURES ...............................................................<br>3|
|---|
|**LIST OF**FIGURES...........................................................................................................................................L3|
|1. -<br>INTRODUCTION.<br>4|
|2. -<br>DESCRIPTION OF SEAPORTS' INFRASTRUCTURE AND SERVICES.<br>6|
|2.1. - ECONOMIC<br>ANALYSIS<br>OF SEAPORTS'<br>ACTVITY.<br>6<br>|
|2.2.- DESCRIPTION OF SEAPORTS' INFRASTRUCTURE.<br>7|
|2.3.-<br>DESCRIPTION OF PORT SERVICES.<br>8|
|2.4. - COORDINATION BETWEEN SEAPORTS' ACTIVITIES: PORT AUTHORITIES .11|
|3. -<br>SEAPORT INDUSTRY REGULATION**.13**|
|3.1.-SITUATION OF SEAPORTS BEFORE PRIVATIZATION AND LIBERALIZATION.13|
|3.2. - FORMS OFPRIVATE PARTICIPATION AT SEAPORTS AND REGULATIONNEEDS.16|
|3.3.- CONCESSION CONTRACT DESIGN .22|
|_3.3.1. - OBJECT_<br>_OF CONCESSION_<br>_.23_<br>|
|_3.3.2.- ExcLusiviTy .24_|
|_3.3.3.- OBLIGAITTNSANDDPAYMENTS .25_|
|_3.3.4. -TERM .26_|
|_3.3.5.-ExcEss_<br>_OF LABOR.26_|
|_3.3.6.-PENALTIESAND FINES._<br>_27_|
|_3.3.7.-RISKALLOCATON .27_|
|_3.3.8.-SLECTboN PRoCEs. .30_|
|_3.3.9.-RE-NEGOTIATioN OF CONCESSION_<br>_CONTRAcTs._<br>_31_|
|3.4. - PRIVATIZATION AND LIBERALIZATION OF SEAPORTS: INTERNATIONAL EXPERIENCES .32|
|_3.4. 1. -SEAPORTS REFORM: THE CASE OF CHILE .33_|
|_3.4.2. -SEAPORTS' REFORM: THE CASE OFARGENTINA .34_|
|_3.43. -SEAPORTS' REFORm: THE CAsE oF BRAziL .36_|
|**4.**-<br>**PRICE REGULATION.37**|
|4.1. - PORT TARIFFS<br>**.37**|
|4.2. - CARGO HANDLiNG CHARGES .39|
|4.3.- CONCESSION FEES .40|
|5. -<br>**REGULATION ON QUALITY AND SAFETY.42**|
|_5.1._- CONGESTION PROBLEMS .42|
|5.2.-<br>QUALITY OF CARGO HANDLING SERVICES .43|
|5.3.-<br>GENERAL PORT SAFETY .44|
|**6.**-<br>**PERFORMANCE INDICATORS.45**|
|6.1.-<br>PHYSICAL INDICATORS .46|
|6.2. - FACTOR PRODUCTIVITY INDICATORS .47|
|6.3. - ECONOMIC AND FINANCIAL<br>INDICATORS<br>.48|
|7. -<br>**REGULATORY INSTITUTIONS.48**|
|**8.**-**APPENDIX 1: TYPE OF OWNERSHIP OF**MAIN 50**WORLD PORTS (1997) ............ .................**<br>_53_|
|_SOURCE:_**CASS (1996) ..............................................................**<br>53|
|**9.**- **APPENDIX 2: SEAPORTS' PRIVATIZATION**- **INTERNATIONAL ..........................................****_54_**|
|**APPENDIX 3: CONCEPT OF**LONG-RUN**MARGINAL COST ............................................................**57|
|**11. - REFERENCES ..............................................................**<br>**59**|

2
Table D5: Main Outcomes, using Attrition-predicted Weights

||(1)<br>Enrollment|(2)<br>Fees|(3)<br>Score|
|---|---|---|---|
|High|8.71|25.69***|0.17*|
||(5.55)|(7.88)|(0.09)|
|Low Treated|16.73**|5.47|-0.04|
||(7.19)|(7.86)|(0.11)|
|Low Untreated|0.91|6.30|0.06|
||(5.27)|(6.40)|(0.07)|
|Baseline|0.77***|0.82***|0.37***|
||(0.04)|(0.04)|(0.11)|
|R-Squared|0.62|0.71|0.16|
|Observations|3878|2230|706|
|# Schools (Rounds)|797 (5)|769 (3)|706 (1)|
|Mean Depvar|163.64|238.13|-0.21|
|Test pval (H=0)|0.12|0.00|0.05|
|Test pval (_L_<sup>_t_ </sup>= 0)|0.02|0.49|0.72|
|Test pval (_L_<sup>_t_ </sup>= H)|0.24|0.01|0.05|

Notes: * p<0.1, ** p<0.05, *** p<0.01 a) This table checks whether our results are robust to accounting for differential attrition using the inverse probability weighting technique. In addition to using saturation or tracking weights, we now weight all regressions with attrition-predicted weights. This procedure is described in detail in Appendix D. Cols 1-3 show impacts on enrollment, fees, and test scores with these weights. b) Regressions are weighted to adjust for sampling, tracking where necessary, and attrition, and include strata and round fixed effects, with standard errors clustered at the village level. The number of observations may vary across columns as data are pooled across rounds and not all outcomes are measured in every round. We thus also report the number of schools and rounds for each regression; any variation in the number of schools arises from attrition or missing values for some variables.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

36
28

# **Appendix Simulation with Regional Poverty Rates Backwards from 2001 Data**

The analysis presented thus far involves simulating changes in poverty forwards from the 1991/92 survey data. Although Datt and Walker do not discuss the possibility in their work, it is also possible to project changes _backwards_ from the 2000/01 data. In terms of the notation used in Section 4, this involves recursive calculations for household consumption and the weights as follows: and For Tanzania, the backwards analysis has one advantage over the forward analysis. Because the 2000/01 HBS is representative at the regional level, regional poverty rates going back in time can be simulated. The earlier HBS is only representative at the stratum level, so it cannot be used to produce region-level estimates.

Year-by-year simulated headcount rates at the regional level are shown in Appendix Table A9, and headcount trajectories are plotted for a sample of regions in A1. The table also shows the standard errors on the 2001 estimates, which are calculated from the actual survey data. Overall, most but not all regions show a pattern mirroring that of changes in the national poverty rate: increases in poverty in the first part of the decade followed by declines in recent years.

These regional poverty rates should be interpreted with great care. In addition to the caveats already mentioned, the standard errors on the regional predictions are large because the regional sample sizes are small. As discussed in the previous section, growth estimates for some regions in certain years (in particular 1999 and 2000) appear to have been obtained by very rough approximations based on national growth rates. In Mtwara, simulated poverty incidence drops at an implausible rate, from 64% in 1997 to 40% in 1999. As there is no baseline with which to compare the results of the regional predictions, it is very difficult to assess their accuracy. There is one region for which this can be done, Dar es Salaam. The backwards simulation implies that the headcount rate for Dar was 10.0% in 1992, while according to the 1991/92 HBS survey it was 28.1% Given the wide divergence between poverty incidence based on regional growth data and the HBS data , the regional results should be taken as broadly suggestive rather than indicative of particular regional trends.
ineffective when the RER undervaluation is larger (say, more than 20 percent). For RER misalignments calculated using our PMG estimates of the long-run RER equation, trade openness becomes positive and significant while liability dollarization is negative and significant. These results may imply that: (a) countries that are more open to trade may be more successful in engineering an undervaluation, (b) the likelihood of undervaluation is smaller in countries that are highly dollarized. The latter result may reflect the ― _fear of floating_ ‖ due to deleterious effects of depreciation on the balance-sheet of countries with high liability dollarization. Finally, it should be pointed out that the measure of exchange rate flexibility is robustly positive and significant, whereas intervention in the FOREX market has a significant effect on the incidence of undervaluation only in the presence of fiscal discipline.

The _Tobit_ analysis shows that policymakers may have a more limited role in influencing the magnitude of the RER undervaluation with either Johansen or PMG estimated RER misalignments. In contrast to our _Probit_ results with Johansen estimated RER misalignments, flexible exchange arrangements and FOREX market intervention have a less robust link with the size of RER undervaluations. The exchange arrangement is mostly not significant in all regressions, while FOREX intervention has a positive and significant effect only while controlling for the fiscal policy stance. With the PMG coefficient estimates of the long-run RER equation capital account openness variables (as measured by the ratio of foreign liabilities to GDP, _TL_ , and foreign assets and liabilities to GDP, _TAL_ ) are positive and significant while the Chinn-Ito index of financial openness is significant in regressions that do not control for fiscal discipline. Moreover, fiscal discipline and liability dollarization have a negative and significant coefficient while trade openness is positive and significant. Intervention is significant only when controlling for fiscal discipline while exchange rate regime has a robustly positive and significant coefficient estimate.

Next, we investigate whether foreign exchange intervention can generate persistent RER deviations from equilibrium. Our _Probit_ analysis shows that RER misalignments may not be easily corrected (hence, deviations may persist) in highly dollarized economies and will dissipate at a slower speed in countries with less flexible arrangements. More specifically, the speed of mean reversion would be slower in countries with fixed regimes in RER overvaluation. In turn, FOREX intervention will also reduce the speed of mean reversion and, therefore, generate a more persistent incidence of undervaluation.

6