rafmacalaba/datause-encoder-data huggingface dataset:
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE b. Power costs <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Total cost<br>US cents per KwH<br>Mali Niger Congo Benin Cape Verde Cameroon Rwanda Burkina Faso Madagascar Kenya Tanzania Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Mozambique Uganda Zimbabwe Nigeria Malawi Ethiopia DRC Zambia South Africa Botswana<br><!-- End of picture text --> _Source:_ Power price: Briceño-Garmendia and Shkaratan 2010; Zimbabwe average tariff derived based on World Bank (2010); power costs: Eberhard and others 2008 _Note:_ DRC = Democratic Republic of Congo.

ZESA also faces difficulties with revenue collection, which deprives the utility of millions of dollars each year. Only 60 percent of ZESA‘s bills are collected, a very poor performance when compared to other low-income countries in Africa (table 2), but nonetheless a slight improvement from the collection rate in the early 2000s, when less than 50 percent of the bills were collected. At present, around 83 percent of the arrears are from the private sector, though nonpayment from other parastastals and the government is a significant issue and creates a perverse tax on utilities.

Fortunately in Zimbabwe network losses are a relatively minor concern as the backbone transmission and distribution network, despite the lack of investment, remains in good condition. Another interesting element is the low system losses, which are in part due to low levels of nontechnical losses. A valid explanation for these small losses could be that the cheap power prices deter power theft.

In 2009 the cumulative effect of these inefficiencies amounted to $485 million, or the equivalent of 100 percent of ZESA‘s revenues (table 3). Over 50 percent of the costs are due to collection inefficiencies while essentially the rest is traceable to tariff subsidies (table 3).

ZESA‘s hidden costs remain high in absolute terms, but they have been coming down in recent years. In 2009 tariffs were increased about $0.01 to current levels of $0.065 per kWh, which led to a substantial reduction in hidden costs. The circumstantial reduction in the volume of power sold also helped to contain hidden costs, though it does not represent any kind of improvement in efficiency. Yet, compared to other southern African countries, hidden costs in 2009 are still high at around 400 percent of sector revenues, second only to those found in the Democratic Republic of Congo (figure 5).

While not included in the estimates of hidden costs in figure 5, labor-related inefficiencies further curtailed ZESA‘s financial security, draining as much as 16 percent from revenues in 2009. As of 2007, ZESA employed around 6,455 staff and was plagued by low staff utilization that reduced the scale of operations. The number of workers per gigawatt-hour of power produced was 0.86, four times larger than the figure for several other countries.

17
# 4 Conclusions

High‐growth firms have a disproportionate ability to create jobs and generate output. But there is less evidence and understanding about whether the benefits to economic growth outweigh the costs they impose on other firms. To understand the impact of HGFs on the economy as a whole, we rely on detailed administrative microdata from Hungary. We find that productivity growth of firms in Hungary is stronger if they serve industries with more HGFs. The fact that the premium in productivity growth is associated with growth in sales and that spillovers exceed local labor markets suggests that the surge in demand generated by HGFs may allow their suppliers to exploit economies of scale. Not only do HGFs have positive spillovers, but they benefit more firms in the middle of the productivity distribution. Firms in the bottom quartile of the distribution are not productive enough to take fuller advantage of HGFs demand. Firms in the top quartile of the distribution have access to wider markets and do not depend on HGFs as much. We also find that manufacturing firms are better able to benefit from the presence of HGFs than services firms. Lastly, we ascertain that the spillover effects are still present at a finer geographical level.

The results are robust to alternative definitions of HGFs, sample and estimation method. As discussed in the paper, establishing causality presents significant econometric challenges. The fact that they continue to hold when controlling for a plethora of factors and are not sensitive to several robustness checks gives us confidence in them.

The paper provides evidence for policy makers who may be concerned about directing scarce resources towards supporting HGFs, instead of using these resources for firms in other parts of the economy. It also highlights that the attention and resources given to HGFs can benefit other firms in the economy.

# References

Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification properties of recent production function estimators. _Econometrica_ , _83_ (6), 2411‐2451.

Ács, Z. 2010. “High‐Impact Entrepreneurship”. Chapter 7 in Z.J. Acs, D.B. Audretsch (eds.), Handbook of <u>Entrepreneurship Research, 165. International Handbook Series on Entrepreneurship 5</u> Angrist, J. D. (2014). The perils of peer effects. _Labor Economics_ , 30, 98‐108.

Audretsch, D. B. 2005. “The Knowledge Spillover Theory of Entrepreneurship and Economic Growth”. _The Emergence of Entrepreneurial Economics: Research on Technological Innovation, Management and Policy_ , Vol. 9: 37‐54.

Autio, E., Kronlund, M., and Kovalainen, A. 2007. “High‐Growth SME Support Initiatives in Nine Countries: Analysis, Categorization, and Recommendations”. Report Prepared for the Finnish Ministry of Trade and Industry. Publications, Industries Department.

<mark>Birch, D. L. (1981). Who creates jobs?</mark> _<mark>The public interest</mark>_ <mark>, (65), 3.</mark> Blalock, G., & Gertler, P. J. (2008). Welfare gains from foreign direct investment through technology transfer to local suppliers. _Journal of International Economics_ , _74_ (2), 402‐421.

21
# **Table 12. Effect of dispersion in customs clearance time on imports**

||<br>Dependent variable is|<br>first‐difference in log i<br>yea|<br>mport value at firm‐HS6<br>r level|<br>product‐origin country‐|<br>Dependent variable is long‐difference in log<br>import value at firm‐HS6 product‐origin<br>country level|
|---|---|---|---|---|---|
||_Clearance time_<br>_measured in days ‐_<br>_2007‐2012 period_|<br>_Clearance time_<br>_measured in hours_<br>_‐ 2010‐2012 period_|<br>_Clearance time_<br>_measured in days ‐_<br>_2007‐2012 period_|<br>_Clearance time_<br>_measured in hours_<br>_‐ 2010‐2012 period_|_Clearance time measured in days ‐_<br>_2007‐2012 period_|
||IV est|imation|IV esti|mation|IV estimation|
||(1)|(2)|(3)|(4)|(5)<br>(6)|
|First‐diff. in interquartile range of clearance||||||
|time|‐1.327***|‐0.027***|‐1.907***|‐0.030**||
||(0.239)|(0.009)|(0.434)|(0.013)||
|First‐diff. in median clearance time (not||||||
|instrumented)|||0.424***|0.003||
||||(0.115)|(0.003)||
|Long‐diff. in interquartile range of clearance||||||
|time|||||‐3.278***<br>‐3.893*|
||||||(1.255)<br>(2.189)|
|Firm*year fixed effects|Yes|Yes|Yes|Yes||
|HS6 product*origin country*year fixed effects|Yes|Yes|Yes|Yes||
|Firm fixed effects|||||No<br>Yes|
|HS6 product*origin country fixed effects|||||Yes<br>Yes|
|Observations|200,281|84,298|200,281|84,298|14,556<br>14,556|
|R‐squared|0.132|0.379|‐0.164|0.36|‐0.948<br>‐0.621|
|F‐statistic from first‐stage regression|170|165|5,874|2,461|18<br>10|
|First‐stage coefficient on first‐diff. in median||||||
|allocation to red channel|0.051***|1.725***|0.033***|1.218***||
||(0.004)|(0.134)|(0.004)|(0.131)||
|First‐stage coefficient on first‐diff. in median||||||
|clearance time (not instrumented)|||0.264***|0.255***||
||||(0.002)|(0.004)||
|First‐stage coefficient on long‐diff. in median||||||
|allocation to red channel|||||0.055***<br>0.046***|
||||||(0.013)<br>(0.015)|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

# **8. Conclusion**

One of the rare successes so far of the Doha “development” round of WTO negotiations is a recently concluded comprehensive agreement on trade facilitation. Most of the reform activities that will take place as a result of this agreement will occur in developing countries. Yet little is known, formally, about the impacts of such reforms. Albania’s reforms - which are already well under way because of the demands of its prospective EU accession - offer important evidence on the effects of trade facilitation measures in a middle-income country. In this study we use a dramatic reduction in physical inspection of imported goods by the Albanian customs authority to estimate the impact of inspections on time and of time on import value. An important feature of the Albanian inspection regime is that it employs risk-based selection procedures. These procedures underpin the reduction in inspections we observe, and they are central to our strategy for econometric identification.

42
were conceived in winter. The least number of children were born during the winter months of December, January and February--these children were conceived in spring. The wedding season in India, which falls in the months from November to February, could partially explain this seasonality of birth (Medora 2003).

Figure 3 shows the proportion of children, among all the children born in a particular month, who died before the age of 3 years. Child mortality declined from 1992 to 2005. In 1992, 14.8 percent of boys and 15.2 percent of girls died before reaching the age of 3 years. By 2005, these figures had declined to 11.5 and 11.8 percent respectively. The highest mortality rates, among children of both genders, were registered in the spring-summer months of April, May, June, and July. Data for boys from 1992 showed that child mortality declined during the winter months and the difference in mortality rates between summer and winter was 2 percentage points.

# **3.  Setting up the Problem**

Figure 4 shows the changes in Z-scores by month of birth for boys and girls in the three rounds of NFHS<sup>7</sup> . Health outcome measures appear to be lowest for children born in summer and improving for children born in fall and early winter. This relationship holds for both girls and boys and for WAZ and HAZ measures. For example, if the average HAZ for boys born in June of 1992 was about -2.27 (standard error of 0.05), HAZ for boys born in December of the same year was -1.73 (SE 0.06). The average HAZ increased from -2.33 (SE 0.05) for girls born in June to -1.69 (SE 0.05) for girls born in December. The differences in health outcomes between summer and fall/early winter months persist in 1992 and 1998 and exhibit a decline in 2005.

Would the effect of the birth month on a child’s health be significant after controlling for the characteristics of a household, a mother, and a child? To find the answer to this question we rely on a standard theoretical framework of household utility maximization that incorporates the production function determining child’s health (Behrman and Deolalikar, 1988). According to that theory, a household utility is a

> **7** A relatively large gap between the anthropometric measures of children born in December and January persists for both WAZ and HAZ in all rounds of NFHS. We were not able to find an explanation for the phenomenon.

6
# **1. Introduction**

Various reforms in the water sector—including at the river basin level—have been implemented in recent years in many countries.<sup>1</sup> This has come following the increased awareness and concerns of both policy makers and water users regarding the state of water. One of the major components of these reforms at the basin level is the decentralization of management to _the lowest appropriate level_ , as widely advocated in the Dublin Principles (ICWE 1992, p 4) and in the World Bank’s Water Resources Policy Paper (1993, p 18), and the Global Water Partnership Working Paper (GWP, 2000). The lowest appropriate level usually implies the involvement of different stakeholders in the basin, including water users, in order to achieve a more sustainable management of the basin’s water resources.<sup>2</sup> Early approaches to river basin management took an essentially scientific and engineering perspective to optimize water resources development and water allocation. Over time, while some efforts succeeded, many were “disappointing,” as Barrow (1998) concluded. This, and the growing scope of river basin management—to include, for example, ecosystem preservation—led to the call for decentralization to the lowest appropriate level as a means for harnessing local initiatives and creating coalitions for development. In fact, of the several documented reforms of river basin management, both decentralization and performance are concepts that are hard to evaluate. A review of the literature on decentralization in the water sector (Mody, 2004) suggests that not many analyses of decentralization of river basin management functions have been undertaken yet, and that this topic is also relatively recent. Recent works have focused mainly on surveys of efforts at the national level to implement Integrated Water Resources Management (GWP, 2004).

The fact that there are only a few documented works of the experiences regarding the process and impact of decentralization in river basins around the world points to a major gap in our knowledge about the costs and benefits of the decentralization. The few analyses and case studies that particularly deal with decentralization include: the Curu, Jaguaribe and Metropolitana River Basins in Brazil (Kemper and Olson, 2000), the Yellow River Basin in China (Zusman, 1998),

> 1 In this paper we do not question the challenges river basin management poses, and the reasons for decentralization, but take decentralization as an objective by itself.  For a justification for the decentralization approach see Blomquist et al. (2005).

> 2 There could be several definitions for decentralization such as the general definition of delegation of decision making from higher to lower level entity.  But we prefer the combination of increased transparency and involvement in decision making of stakeholders, as it provides a wider basis for measuring decentralization.  Our definition of decentralization and it rationale are elaborated in the theory section of the paper.

4
# **What I Really Want: Policy Maker Views on Education in East Asia Pacific**

_Noah Yarrow, Paul Cahu, Mary Breeding and Rythia Afkar_ World Bank JEL Codes: D73 (Bureaucracy), H52 (Government Expenditures and Education), I21 (Analysis of Education), I22 (Educational Finance) I25 (Education and Economic Development) I28 (Education Government Policy)

> _Keywords:_ education, bureaucracy, policy preferences, discrete choice

Acknowledgments: The authors benefited from useful comments on the analysis from Andrew D. Mason, Toby Linden, and Lars Sondergaard. The authors also appreciate Halsey Rodgers, Sergio Venegas Marin and Thomas Walker for their comments and suggestions on the draft version of this paper, and Cristian Aedo for his guidance. 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. This paper is a product of the Education GP. It is part of a larger effort by the World Bank to provide open access to its research and contribute to development policy discussions around the world. Policy Research Working Papers are posted on the Web at http://www.worldbank.org/prwp. The author(s) may be contacted at nyarrow@worldbank.org.
**Figure 2. Tax Revenues as a Percentage of GDP, 1990-2000**

<!-- Start of picture text --> 30.00%<br>25.00%<br>20.00%<br>SUNAT<br>SAT<br>SENIAT<br>15.00%<br>URA<br>KRA<br>SARS<br>10.00%<br>5.00%<br>0.00%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text --> The URA had one of the most spectacular increases in revenues as a percentage of GDP: from 7.0% in 1991 to 11.9% in 1999. Over the course of the 1990s, however, tax policy changes, including the introduction of the VAT (and the subsequent increase in VAT coverage) and the reduction in the number of exemptions, also played a role in boosting revenues.

In Kenya total revenues declined from 27% of GDP in 1994 to 21% in 2000, though the decrease was due largely to major tax policy reforms, including the significant reduction of tax rates across the board (the reduction of the top marginal income tax rate from 40% to 30%, the top import tariff rate from 45% to 25%, and the standard VAT rate from 18% in 1995 to 15% in 2000). The accomplishment of the KRA was to maintain revenues by increasing administrative efficiency in the context of drastically reduced tax rates. Consequently, the KRA exceeded its revenue targets in three years over the five year period (1996-2000) after its establishment. Indeed, though the VAT rate standard rate was 15% in both years, the ratio of total VAT collections to GDP increased from 5.0% to 5.4% from 1997 to 2000.

In South Africa the share of tax revenues to GDP decreased slightly from 24.8% to 24.2% over the period 1995-2001, owing largely to changes in tax policies, the net 28
# **Table 3: Number of Procedures, Time and Cost by Income Group**

|||Pro<br>Starting<br>|cedures<br>|||<br>|Time<br>||C|ost as%o<br>Startin<br>|f GNIper ca<br>|pita|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Getting|a|Registeri||Getting|Starting|Registeri|||g a|Registerin||
||<br>Electricit<br>y|Busines<br>s|ng<br>Property|Constructi<br>on Permits|<br>Electrici<br>ty|<br>a<br>Business|ng<br>Property|Constructi<br>on Permits|Getting<br>Electricity|<br>Busine<br>ss|g<br>Property|Construction<br>Permits|
|**Entire **|**Sample**||||||||||||
|Obs|183.0|183.0|180.0|182.0|183.0|183.0|180.0|182.0|183.0|183.0|180.0|182.0|
|Mean|5.4|7.8|5.9|15.7|111.4|34.0|57.9|192.7|2,158.0|42.2|6.0|486.0|
|Std.|1.5|3.3|2.3|6.5|80.0|58.7|64.9|130.1|4,514.7|74.3|4.9|1,073.1|
|Min|3.0|1.0|1.0|6.0|17.0|1.0|1.0|26.0|0.0|-|-|0.9|
|Max|11.0|18.0|15.0|50.0|585.0|694.0|513.0|1,179.0|36,696.7|735.1|27.9|7,553.7|
|**Low-In**|**come**||||||||||||
|Obs|32.0|32.0|32.0|31.0|32.0|32.0|32.0|31.0|32.0|32.0|32.0|31.0|
|Mean|5.3|8.5|6.5|14.8|163.1|40.4|87.1|252.3|7,902.5|128.4|8.5|1,354.3|
|Std.|1.5|3.7|2.3|5.0|123.7|43.3|80.4|218.7|8,206.8|132.4|4.9|2,078.5|
|Min|3.0|2.0|3.0|8.0|30.0|3.0|5.0|75.0|1,461.8|3.7|0.4|47.9|
|Max|9.0|17.0|12.0|29.0|585.0|216.0|301.0|1,179.0|36,696.7|735.1|18.5|7,553.7|
|**Lower**|**Middle Inc**|**ome**|||||||||||
|Obs|54.0|54.0|51.0|54.0|54.0|54.0|51.0|54.0|54.0|54.0|51.0|54.0|
|Mean|5.7|8.3|6.1|16.4|101.1|37.5|70.4|177.8|1,962.1|46.0|6.2|545.5|
|Std.|1.7|2.8|2.4|5.9|62.6|31.5|75.9|85.3|2,061.5|43.4|6.0|830.3|
|Min|3.0|3.0|1.0|6.0|33.0|3.0|2.0|51.0|74.2|3.1|-|22.4|
|Max|11.0|17.0|15.0|34.0|285.0|161.0|513.0|475.0|8,997.0|169.9|27.9|3,758.4|
|**Upper**|**Middle Inc**|**ome**|||||||||||
|Obs|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|
|Mean|5.6|8.0|6.3|17.0|107.3|40.6|38.8|201.9|620.4|15.6|5.7|257.9|
|Std.|1.4|3.2|2.0|7.8|61.2|98.3|30.5|103.1|664.6|19.9|4.3|397.4|
|Min|3.0|3.0|2.0|7.0|18.0|3.0|2.0|46.0|15.4|1.0|-|5.4|
|Max|10.0|17.0|13.0|50.0|281.0|694.0|197.0|483.0|4,125.4|119.9|16.9|1,690.0|
|**High In**|**come**||||||||||||
|<br>Obs|<br>47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|
|Mean|4.8|6.4|4.9|14.2|92.4|18.5|44.7|160.8|107.6|7.3|4.4|87.6|
|Std.|1.0|3.5|2.3|6.3|64.3|24.1|58.7|109.2|144.1|15.0|3.6|141.9|
|Min|3.0|1.0|1.0|6.0|17.0|1.0|1.0|26.0|0.0|-|-|0.9|
|Max|8.0|18.0|11.0|33.0|279.0|135.0|298.0|677.0|833.3|100.9|12.7|589.4|

34
# **Determinants of export growth at the extensive and intensive margins: Evidence from product and firm-level data for Pakistan**

# **1. Introduction**

Trade and export performance of Pakistan in the last 15-20 years has not been positive, as documented in several reports (Planning Commission, 2011, World Bank 2007, Manes 2010, Lahore University 2009). Given that trade is a key driver of growth and development – most, if not all, success stories in growth accelerations worldwide have an outward-looking strategy; analyzing in detail and understanding the reason for this performance is an integral part of the growth and development agenda for Pakistan.

This paper presents a comprehensive and detailed analysis of trade and specially exports performance in Pakistan in the last ten years. The analysis of trade outcomes is intended to guide a systematic generation of hypotheses about Pakistan’s export performance, prospects, and challenges. Following Reis and Farole (2012), the analysis uses the decomposition of the margins of trade growth as a framework for exploring trade competitiveness. Specifically, in this note we will review four main dimensions on which Pakistan’s country’s trade competitiveness performance can be determined: 1.) the **_level, growth, and market share_** performance of existing exports (the “intensive margin”) of exports as well as _market share_ performance; 2.) **_diversification_** of products and markets (the “extensive margin”); 3.) the **_quality and sophistication_** of exports (the “quality margin”); 4) dimension – the **_entry and survival_** of new exporters (the “sustainability margin”).

The principal source for the analysis is the UN COMTRADE database available through the World Integrated Trade Solutions (WITS) platform<sup>2</sup> . For the entry and survival analysis (sustainability margin) we use firm-level data, based on a customs transactions database for the period 2001-2010. Throughout this note, the performance of Pakistan’s exports is compared to a number of “peer” countries, selected based on factors including: location, similarity in country characteristics (level of income, size, geography, sectoral structure), and their presence as competitors to Pakistan’s exporters in key markets. Due to lack of data availability, not all countries are included across all indicators. Peer countries for comparison using aggregate flows are China, India, Vietnam and Indonesia. In the case of firm level analysis, data availability is more limited, but we were still able to select middle income countries like Bulgaria, Colombia, Peru and South Africa.

> 2 Unless stated otherwise, COMTRADE is the data source for the graphs and figures, with authors’ calculation. Export information is obtained from mirror data.

2