rafmacalaba/datause-encoder-data huggingface dataset:
- McCullough, B. D. (2006): “A Review of TESTU01,” _Journal of Applied Econometrics_ , 21(5), 677–682.

- Moler, C. (2007): “Parallel MATLAB: Multiple processors and multiple cores,” _The MathWorks News and Notes_ , June.

- Odeh, O. O., A. M. Featherstone, and J. S. Bergtold (2010): “Reliability of Statistical Software,” _American Journal of Agricultural Economics_ , 92(5), 1472–1489.

- Ripley, B. D. (1990): “Thoughts on pseudorandom number generators,” _Journal of Computational and Applied Mathematics_ , 31(1), 153–163.

- Smeeton, N., and N. J. Cox (2003): “Do-it-yourself shuffling and the number of runs under randomness,” _Stata Journal_ , 3(3), 270–277.

- StataCorp (2009): _Stata data-management reference manual: Release 11_ . StataCorp LP, College Station, TX.

- Tirler, G., P. Dalgaard, W. H ormann, and J. Leydold (2004): “An Error in the Kinderman-Ramage Method and How to Fix It,” _Computational Statistics and Data Analysis_ , 47(3), 433–440.

11
Simulation 3 is an _increase in transfers for all kids eligible to attend public school to compensate for the current non-fee expenses_ (such as books and uniforms). This corresponds to giving each child in public school a subsidy equivalent to the average monetary cost of non-fee expenses in public and private schools.<sup>9</sup> Simulation 4 is _a pure redistributive scenario, in which all public resources channeled into beneficiaries of urban public schools are redistributed to children attending rural public schools on a per capita basis_ .<sup>10</sup> This is not to say that this is a realistic or desirable policy; rather, the scenario provides a sense of the redistributive potential that such a ̳draconian‘ intervention would have.

# **[Table 4 about here]**

Results in Table 4 report the _average_ probability to attend public school for each group before and after the shock, and the respective changes on HOI attributed to the shock.<sup>11</sup> Also, it provides an indication of the additional per beneficiary fiscal cost associated with each scenario. A first finding is that the overall impact in all four simulations is quite limited in magnitude, even when they involve large swings in resources (simulation 4) or large relative increases in transfers (simulations 1 to 3). This implies that even when changes are substantial in relative

> 9 We could have also used the average spending in non-fee expenses only in public school. This would have biased downwards the cost of diminishing inequality, since children in public school are likely to spend less than the presumably more appropriate amount spent in better-equipped private schools.

> 10 Note that we base our exercise on estimates for children in public schools, since it is assumed that the government will not assign policies that affect the children already in the private system that might be paying fees to private entities. This assumption limits the extent of the policy effect, but seems more reasonable in terms of policy implementation.

> 11 Following the recent literature on the HOI, we used the geometric mean version of the index for the simulations in Table 4. The geometric HOI respects the property of sub-group consistence and is not weakly sensitive to inequality as in the linear version of the index.

25
4 Third, the trends in the indicators are generally consistent with each other, though there are some important exceptions. In the four countries experiencing economic growth (Ethiopia, Ghana, Mauritania and Uganda) the trends in human development indicators match the improvement in economic well-being. But in those experiencing stagnation and decline, the signals are noisier. In some cases the education indicator improved despite the decline in economic living standards (Madagascar, Nigeria and Zimbabwe). Child mortality improved in Zambia and child malnutrition improved in Zimbabwe during episodes of deteriorating economic circumstance. Such outcomes serve as a reminder that focusing only on one dimension of well-being can be misleading when tracking poverty dynamics over time (World Bank, 2000).

# **_Inequality in human development_**

The indicators in Table 1 are averages for the population as a whole. We now review the _distribution_ of these indicators across the populations, identifying especially changes in the welfare of poorer households. We begin with the human development indicators. Primary school enrollments are particularly low in Ethiopia (Table 2), and to a lesser extent in Mauritania. The poorest households in these countries typically do not enroll their children in primary schools. But there have been major strides in raising primary enrollments during the decade in Ghana, Madagascar, Mauritania, and Uganda. And where there have been education enrollment gains, they have included the poor. Only Zambia seems to have lost ground.

**Table 2. Primary net enrollment rates by consumption quintile for seven African countries**

||_Ethi_|_opia_|_Gh_|_ana_|_Mada_|_gascar_|_Maur_|_itania_|_Uga_|_nda_|_Zam_|_bia_|_Zimb_|_abwe_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_SurveyYear_|_1996_|_1997_|_1992_|_1998_|_1993_|_1999_|_1987_|_1995_|_1992_|_1997_|_1993_|_1998_|_1991_|_1996_|
|Poorest quintile|15.0|17.0|54.4|70.1|29.3|53.2|19.4|25.0|54.4|79.6|57.5|49.8|77.9|80.8|
|Secondquintile|15.0|24.0|69.1|81.2|43.4|64.8|25.4|40.8|63.2|87.0|67.1|61.6|82.2|84.6|
|Thirdquintile|18.0|27.0|73.0|86.3|59.0|64.0|29.4|48.6|68.9|88.0|75.2|68.6|83.6|87.2|
|Fourth quintile|21.0|28.0|76.8|87.1|59.7|68.0|31.6|49.6|74.7|86.6|81.9|74.8|85.7|89.2|
|Richestquintile|30.0|33.0|87.2|90.2|59.6|77.7|40.9|60.0|85.7|89.4|86.0|80.7|89.0|90.9|
|_Ql/Q5_|_0.50_|_0.52_|_0.62_|_0.78_|_0.49_|_0.68_|_0.47_|_0.42_|_0.63_|_0.89_|_0.67_|_0.62_|_0.88_|_0.89_|

Source: Country studies under Dynamics of Poverty study (see bibliography).

Because income data were not collected in the Demographic and Health Surveys, Sahn et al. (1999) constructed a proxy index for income based on assets and household amenities. This enabled them to examine trends in child health capabilities (survival and nutrition) by wealth class. The poorest 20 percent of the populations appear to be the worst affected by the deterioration in pre-school child nutrition (Table 3). Stunting (measured by height-for-age) has
- Easterly, W. (2003): “Can Foreign Aid Buy Growth?,” _Journal of Economic Perspectives_ , 17(3), 23–48.

- Glicksberg, I. L. (1952): “A further Generalization of the Kakutani Fixed Point Theorem with Application to Nash Equilibrium Points,” _Proceedings of the National Academy of Sciences_ , 38, 170–174.

- Heckelman, J., and S. Knack (2008): “Foreign Aid and Market-Liberalizing Policy Reform,” _Economica_ , 75, 524–548.

- Knack, S. (2001): “Aid Dependence and the Quality of Governance: A Cross-Country Empirical Analysis,” _Southern Economic Journal_ , 68, 310–329.

- Knack, S., H. F. Rogers, and N. Eubank (2011): “Aid Quality and Donor Rankings,” _World Development_ , 39, 1907–1917.

-  ̈Ohler, H., P. Nunnenkamp, and A. Dreher (2012): “Does conditionality work? A test for an innovative US aid scheme,” _European Economic Review_ , 56(1), 138 – 153.

- Strange, A., B. Parks, C. Perla, and H. Desai (2015): “AidDatas Methodology for Tracking Underreported Financial Flows,” AidData, Willamsburg VA.

- Tavares, J. (2003): “Does Foreign Aid Corrupt?,” _Economic Letters_ , 79(1), 99–106.

- World Bank (1998): _Assessing Aid: What Works, What Doesn’t, and Why. A World Bank Policy Research Report_ . Oxford University Press, Washington, DC.

- (2005): “Global Monitoring Report 2005,” Washington DC.

25
hardships arising from gas price hike on households that rely heavily on gas for heating, cooking and lighting.

**Table 1: Recent Gas Tariff Increases in Armenia**

||**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|
|---|---|---|---|---|---|---|---|---|---|
|Import price (USD per<br>1000 m3)|||55|55|110|110|110|154|180|
|_Change (%, Increase)_|||||_100_|||_40_|_17_|
|**Tariff**||||||||||
|for small consumers<br>(<10,000 m3 per month),<br>1000 AMDper 1000 m3|51|51|59|59|90|84|84|96|132|
|_Change (%, Increase)_|||_16_||_53_|_-7_||_14_|_38_|
|for large consumers<br>(>=10,000 m3 per<br>month), USD per 1000<br>m3|79|79|79|79|147|153|153|215|243|
|_Change (%, Increase)_|||||_85_|_3_||_40_|_13_|
|Date the tariff became<br>effective|Jan 1,<br>2000||Mar 1,<br>2004||Apr 10,<br>2006|Jan 1,<br>2007||Apr 1,<br>2009|Apr 1,<br>2010|

_Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia Using data from a nationally representative household survey of 2008 and national energy statistics, the paper provides an overview of household energy consumption patterns, highlights Armenia’s energy vulnerability, and analyzes the poverty and distributional impact of the latest gas price hike.<sup>2</sup> The higher gas price is anticipated to significantly increase the share of energy expenditures of most households, particularly during the cold winter months. Heating during the cold winter months accounts for the bulk of household energy use in Armenia (Sargsyan, Balabanyan and Hankinson, 2006). Households living in urban and rural areas are likely to face different choices of energy sources, as would those households living in different housing conditions. The results of the analysis show that the April 2010 gas price increase would result in a significant increase in households' energy expenditures and would have an adverse impact on poverty. The paper draws policy implications for designing of mitigation measures to

> 2 Baclajanschi et al (2006) undertook similar analysis of gas price hike in Moldova.

3
# **Table 9. Poor and vulnerable groups**

||Overa<br>|ll|Non|poor|P|oor|Extr<br>p|emely<br>oor|Vuln<br>to p|erable<br>overty|<br>Vuln<br>to e<br>po|erable<br>xtreme<br>verty|Hi<br>vuln<br>to p|ghly<br>erable<br>overty|H<br>vuln<br>to e<br>po|ighly<br>erable<br>xtreme<br>verty|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||N<br>('00,000)|%|N|%|N|%|N|%|N|%|N|%|N|%|N|%|
|Overall|79.3|100.0|22.6|100.0|56.7|100.0|27.4|100.0|56.7|100.0|27.5|100.0|51.3|100.0|19.7|100.0|
|_Household and individual characteristics (fro_|_m DHS 2005)_||||||||||||||||
|Region|||||||||||||||||
|West|12.0|15.1|3.1|13.8|8.9|15.6|4.1|15.1|9.0|15.9|4.5|16.4|8.1|15.7|3.2|16.2|
|Southeast|4.4|5.6|0.6|2.5|3.9|6.9|2.1|7.7|3.9|6.8|2.1|7.5|3.8|7.3|1.6|8.0|
|North|7.8|9.9|1.5|6.9|6.3|11.1|3.4|12.4|6.3|11.1|3.3|12.0|6.0|11.8|2.7|13.5|
|Northeast|2.7|3.4|0.5|2.1|2.2|3.9|1.1|4.1|2.2|3.9|1.2|4.2|2.1|4.0|0.9|4.4|
|Artibonite|12.9|16.3|2.4|10.8|10.5|18.5|5.0|18.4|10.3|18.2|4.9|17.7|9.4|18.4|3.5|17.7|
|Center|6.7|8.5|0.5|2.4|6.2|10.9|4.3|15.9|6.2|10.8|4.3|15.8|5.9|11.4|3.5|17.6|
|South|5.7|7.2|1.0|4.4|4.7|8.3|1.9|7.1|4.6|8.2|2.1|7.5|4.3|8.4|1.3|6.5|
|Grand-Anse|5.4|6.9|0.6|2.8|4.8|8.5|2.9|10.6|4.8|8.4|2.8|10.3|4.4|8.7|1.9|9.5|
|Northwest|4.7|6.0|0.7|3.0|4.1|7.2|2.3|8.3|4.0|7.0|2.1|7.7|3.6|7.0|1.2|6.2|
|Port-au-Prince|16.8|16.8|21.3|11.6|51.5|<br>5.2|9.2|0.1|0.4|5.5|9.6|0.3|1.1|3.8|7.3|0.1|
|Area of residence|||||||||||||||||
|Urbain|31.5|39.8|17.2|76.1|14.4|25.3|1.9|7.0|14.7|25.9|2.2|8.1|11.7|22.8|1.2|6.2|
|Rural|47.8|60.2|5.4|24.0|42.4|74.7|25.5|93.0|42.1|74.2|25.3|91.9|39.6|77.2|18.5|93.8|
|Head of households|||||||||||||||||
|Male|45.6|57.5|11.7|51.6|33.9|59.8|16.5|60.4|33.8|59.6|16.6|60.4|30.8|60.0|11.6|58.9|
|Female|33.7|42.5|10.9|48.4|22.8|40.2|10.9|39.6|22.9|40.4|10.9|39.6|20.6|40.1|8.1|41.1|
|Education of head of household|||||||||||||||||
|No education|33.1|41.8|2.8|12.2|30.4|53.6|21.2|77.3|30.3|53.5|20.6|74.7|29.4|57.2|16.6|84.1|
|Primary|37.7|47.5|13.1|58.1|24.5|43.3|6.2|22.7|24.6|43.5|6.9|25.1|20.8|40.5|3.1|15.9|
|<br>Secondary and above|8.5|8.5|10.7|6.7|29.7|<br>1.8|3.2|0.0|0.0|1.7|3.1|0.0|0.1|1.2|2.3|0.0|
|<br>0-5 years old|10.3|13.0|2.3|10.1|8.1|14.2|4.2|15.4|8.0|14.2|4.2|15.3|7.3|14.3|3.0|15.4|
|<br>Malnutrition (stunting)||1.0|23.2|0.1|9.4|0.9|27.4|<br>0.6|35.3|<br>0.9|27.2|<br>0.6|34.9|<br>0.8|28.4|<br>0.4|
|Mortality||10.1||6.7||11.5||13.7||11.4||13.5||12.3||13.7|

20
Good PETS are driven by specific policy questions allowing the identification of the point(s) at which substantial leakages occur, and ideally why, thereby enabling appropriate solutions to be found (Savedoff 2008). A study in Chad tracked resource flows and revealed considerable fund leakages (Gauthier and Wane 2008). The findings helped determine which flows were more leakage prone, and at what points in the health system leakages occurred (Table 6). Resource flows included financial resources, medical materials, and main medications allocated to health care providers by the Ministry of Health (Gauthier and Wane 2008).

Frontline service providers received only 1 percent out of the central non-wage recurrent budget compared to 33 percent of total recurrent resources, and the regional administrations received 27 percent of non-wage resources compared to 50 percent of total recurrent resources (Gauthier and Wane 2006). This suggests that wages are less susceptible to leakage than non-wage funds, likely because workers know the size of their payments and have strong incentives to put pressure (when they have the means to do so) on officials to ensure delivery of their wages.<sup>10</sup> Merely 5 percent of the resources received by the regional administrations were delivered to health facilities implying diversion of budgeted resources due to weak administration, mismanagement, and absence of oversight and audits at all levels of the health system.

**<u>TABLE 6. RESOURCE LEAKAGES IN THE HEALTH SECTOR IN CHAD, 2003</u>**

||**Resources received at regional**<br>**level**<br>**(regional administration)**|**Resources recei**<br>**cente**<br>**(frontline pr**|**ved by health**<br>**rs**<br>**oviders)**|
|---|---|---|---|
||**Non-wage**<br>**Total**|**Non-wage**|**Total**|
|% of resources received from<br>previous level|27<br>50|5|65|
|% of resources received from<br>central level allocation|-<br>-|1|33|

Source: Adapted from Gauthier and Wane (2008).

The common theme of PETS is that they detect leakages, an indicator of underlying governance failures in the form of missing incentives to track, record, and manage resources and the absence of performance information on which to base any sanctions, without which accountability of officials for ensuring that resources reach their intended destination does not exist.

# _Payroll irregularities_

Physicians and nurses listed on payroll and being paid but who no longer, or never worked at a facility lead to payroll leakages due to the absence of accurate and updated employee records, functioning information systems, payroll controls, and internal and

> 10 Gauthier and Wane (2008: 58) define leakages as “how much the intended beneficiaries received versus how much they should have received as given by resources earmarked for them”.

19
developing country. **Assume** that the before-ta price of blcycles, which must be imported, is $80. The Import tariff in at least one developing country is 70 percent, bringing the total price to $136. I will araL l that farmer is willing to pay $80 for the bicycle, but is unwilling to pay the rull $136 price. In fact, this farmer will pay up to $100, but blanch at any price higher.

What is the efficiency cost of this tax? It is quite easily calculated. Without the tax, the farmer would buy the bicycle for $80. Since he would have paid $100 for the same bicycle, he enjoys a "consumer surplus" of $20; the excess of what the bicycle is worth to him (i.e., what he would have paid) over the price actually paid. When government imposes the 70 percent tax, the farmer no longer purchases the bicycle, and no longer enjoys the "consumer surplus" of $20. The government collects no additional revenue (at least from this potential taxpayer); since the bicycle is not purchased, no taxes are paid. The farmer loses $20 and the government gains nothing.

This story which applies to only one individual can be generalized to the countrywide market for bicycles. Some will be willing to spend more than $100 (or even more than $136), while others are willing to spend less. We can array each individual looking for a bicycle during the year as in Figure 1, with the vertical height being the maximum each farmer is willing to pay (i.e., the demand curve). The numbers are based loosely on trade data from one African country.

Assume 7000 bicycles will be sold without any tax. If there is
Policy Research Working Paper 9645

# **Abstract**

Does a global value chain framework provide additional insights into the question of whether foreign direct investment is beneficial to host countries? The literature has found mixed results on whether foreign direct investment provides positive spillovers over and above mere financing. But the studies have focused on one country, or studies with an international focus tend to abstract from intersectoral linkages. By examining this question in the context of global value chains, this paper provides a much better understanding of the association as well as general validity. It harmonizes three major panel data sets: 1) the Multi-Regional Input-Output table for international input-output linkages, 2) the FDI Markets reports for greenfield foreign direct investment, and 3) the World Bank Enterprise Surveys for firm performance measures. The paper produces a rich panel data set from 2011 to 2017. The findings show that foreign direct investment has a positive effect on labor productivity in sectors and firms within those sectors. Moreover, global value chain participation plays a key role in shaping the foreign direct investment effects. Sectors with lower global value chain participation benefit more from foreign direct investment: doubling the foreign direct investment in those sectors results in an 8 percent productivity gain. The positive effect seems to be due to the increased competition created by foreign direct investment. Foreign direct investment spillovers also take place through domestic and foreign backward linkages, which means that foreign direct investment also has positive inter-sector and cross-border spillovers.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vmercerblackman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
WPS3830

# International Benchmarking of South Africa's Infrastructure Performance

## by

## Željko Bogetić and Johannes W. Fedderke

Abstract: The paper provides a first systematic, comprehensive international benchmarking of South Africa’s infrastructure performance. The analysis focuses on four major sectors––electricity, water and sanitation, information and communication technology, and transportation. The benchmarking is performed against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. Specifically, we seek to answer a number of relevant questions: How does South Africa compare on major indicators of infrastructure sector performance against the relevant country groups? What do outcome indicators tell us about the relative strengths and weaknesses of South Africa’s infrastructure vis-à-vis various income and geographical comparator groups of countries? Where are the largest deviations—positive and negative––from the benchmarks and other comparators? And how does one interpret some of these comparisons to be useful for policy purposes? The results suggest the following. Despite recent gains, access remains a major issue in electricity and, especially, in water and sanitation (particularly in rural areas), and so does performance in local telecom services. Even transport performance appears comparatively less strong than would be expected in an upper middle-income, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport. The main policy implication is that there remain significant needs to scale up infrastructure investments––especially in the yet unserved areas––and improve efficiency in all four major infrastructure sectors if South Africa’s infrastructure performance is to catch up with its group of upper middle-income countries.

## JEL classification: H54, H, R, O, O57

### World Bank Policy Research Working Paper 3830, February 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Authors: Bogetić is lead economist at the World Bank, Africa Region, Washington D.C. (E-mail: zbogetic@worldbank.org) and Fedderke is professor of economics at the University of Cape Town, School of Economics (E-mail: jfedderk@commerce.uct.ac.za). Research assistance by Atsede AemroSelassie is gratefully acknowledged.
# **Appendix 1: Additional Outcomes**

_Table A1.1: Impacts on Other Socio-Emotional Skills (Z-Scores)_

||Frustration Reaction|Motivation Frame-<br>work|Locus of Control|Present Bias|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Treatment 1|-0.034|-0.009|-0.040**|-0.023|
|“Student Self-Learning”|_(0.023)_|_(0.021)_|_(0.019)_|_(0.020)_|
|Treatment 2|0.003|0.063***|-0.001|0.040**|
|“Teacher Delivery”|_(0.021)_|_(0.020)_|_(0.017)_|_(0.020)_|
|N|23,832|23,909|23,724|23,335|
|N Control|9,277|9,378|9,238|9,174|
|N Treatment 1|6,953|6,890|6,921|6,745|
|N Treatment 2|7,602|7,641|7,565|7,416|
|_R2_|_0.210_|_0.366_|_0.292_|_0.200_|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level. All figures are rounded to three decimal places.

_Source:_ Own survey data, school year 2015/2016, own calculations.

32