rafmacalaba/datause-encoder-data huggingface dataset:
## **Introduction**<sup>**2**</sup>

In 1974 the World Bank published a prominent study: “Redistribution with Growth”. It set out thinking about achieving the Holy Grail of development economics – how to obtain overall growth and a better life for all at the same time.

The developmental discourse faces two issues. Why would powerful people heed the call of technocrats for policy reform when it does not explicitly aim at assuring that they come out well? Not only that, but standard developmental discourse has recently emphasized that corruption is a scourge that prevents countries from growing. If this type of enrichment of the powerful may actually be at odds with technocratic policy advice, why follow the advice?

Many people actually running bits of the world want power and wealth. The spectacular growth performance of many countries across the globe has shown that traditional ways of ruling and robbing a country may not be very effective at reaching those goals. How then can enrichment best be pursued by powerful elites?

One might call the goal of the greedy ruler “Enrichment with Growth”. In the following I set out arguments that a Consigliere to the “rich and shameless” might want to think about. First, I discuss key mechanisms that characterize “traditional” corruption. Second, I set out an approach to enrichment that nowadays seems to be followed by many autocrats – ownership of professionally managed corporations – and its limitations. Third, arguments are provided for a potentially more promising approach to enrichment, one that looks suspiciously like some forms of industrial policy. Fourth, the most effective form of enrichment is described – running an economy like a portfolio manager with decent corporate governance. Finally, the paper concludes with perspectives on the debate about enrichment of powerful people, growth and legitimacy.

# **Extracting Rents and Effects on Economic Performance**

Corruption is supposed to make the corrupt better off. In doing so it may undermine the welfare of others, not just to the degree of direct stealing, but by destroying the very basis for economic activity. It is, for example, utterly clear that systems of what Mancur Olson called “roving bandits” fail (Olson, 2000). Competing rent-extracting “warlords” destroy the basis for wealth and end up with little wealth themselves compared to what can be obtained in well-functioning countries (see Shleifer and Vishny (1993) for a basic model).

The warlords themselves are typically not terribly rich by the standards of today’s world and they are somewhat limited in enjoying the fruit of their stealing. They might be interested in

> 2 I am indebted to Alexander Libman and Ignacio Mas for helpful comments

3
FoDowing the completion of field work for this study in 1990, CECPS has continued to refine its field surveys, in terms of both the survey instruments tnemselves and their design and analysis. Nonetheless, the current study demonstrates the power of this type of survey to generate policyrelevant information. This is perhaps best exemplified in the study by the startlingly high importance firms place on the obstacle imposed by tax regulation compared to labor regulation: in the institutional environment of CMte d'Ivoire, apparently restrictive labor law is avoidable, while imposing tax requirements are not.

Finally, since this sticly and other studies on competitiveness and the regulatory framework were carried out in 1990, a number of policy changes have been agreed to by the government of CMte d'Ivoire aimed at easing constraints to the private sector. These measures include: competitivenessoriented reform of labor regulation (including hiring, firing and overtime regulations); reduction of taxes on labor, profits and commercial transactions; commitmen **t** o a program of divestiture of a number of state-owned enterprises, endorsement of a human resources development plan moving public expenditure away from elite-oriented services towards more socially productive and equitable ends; and financial sector reform.

3
Technology may help with these issues.

First, technology can help to identify and value properties. In cases where there is no cadaster, satellite images may be used to produce a map of buildings, with additional ground-truthing to populate information on property characteristics. Traditional valuation approaches such as those based on construction costs are costly and time consuming. In other settings, property owners are asked to self-declare their property value but this is subject to manipulation. Alternatively, a fixed fee may be imposed on each dwelling, but this approach is likely to be regressive. Computer assisted mass appraisal methods that use statistical models to assign an estimated market value to property using data on property characteristics (location, size, amenities etc.) and sale price from recent transactions are widely used in middle- and high-income countries, but in many African contexts with weak property markets, the required data on recent purchase prices are often not available.

An alternative approach aided by technology is a simplified “points-based” system recently adopted by the Freetown City Council in Sierra Leone (Fish, 2018), with support from the International Growth Center, the International Centre for Tax and Development and the UK government (Grieco et al., 2019). The project involved the city’s authorities using satellite imagery to identify and measure properties in Freetown. They then deployed teams to collect data on easily observable characteristics such as the quality of walls, roofs and windows. Following this, they used existing data on rental values to build a simple model for calculating the taxable value of each property. This approach almost doubled the number of properties on the tax register from 57,000 to about 110,000. Crucially, the new system produced much higher tax bills for the most valuable properties, owned by the wealthiest taxpayers, which have been historically under-taxed, thereby increasing the progressivity of the tax system (Kamara, Meriggi and Prichard, 2020).

A similar example comes from Senegal where Knebelmann, Pouliquen and Sarr (2021) report that, at most, a third of cadastral plots in Dakar are on the valuation roll. In addition, rental values listed on the roll are on average lower than values obtained through other sources 12
2016, the price of fuel oil significantly exceeded the import price as domestic oil prices were not adjusted to reflect the drop in international prices. However, the government recently allowed independent power producers to procure oil from abroad at international prices. This led to a dramatic decline in oil imports by the state‐owned Bangladesh Oil and Gas Corporation (BOGC which also goes by the name of _Petrobangla_ ) and an effective reduction in the price of oil paid by the power sector. Bangladesh regulates the prices of all forms of energy, including power prices. The prices of electricity are set based on a cost‐ plus basis but regulation continues to factor in a government subsidy for electricity use. This subsidy has implied below‐cost pricing for consumers and direct transfers to the state‐owned company involved in power purchase and distribution.

Figure 3. Fuel prices<sup>5</sup> <!-- Start of picture text --> 25<br>Domestic Diesel<br>20<br>Price<br>Diesel Import Price<br>15<br>Domestic Fuel Oil<br>Price<br>10 Fuel Oil Import<br>Price<br>Domestic Gas‐to‐<br>5<br>Power Price<br>LNG Price<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>US$/MMBtu<br><!-- End of picture text --> Our model’s assumptions reflect the above features of the power sector in Bangladesh in a number of important ways. First, we account for the transition from a predominantly gas‐based generation fleet to an increasing reliance on coal, LNG and direct import of power in the supply mix. We also incorporate near‐term plans for expanding the fuel‐based power generation. Second, we incorporate the changes in fuel oil price policies and use the international power prices as an input to the model.

# Transmission network

Despite having a population of 163 million (in 2016), Bangladesh’s land area is only 147,000 square km, which is roughly the size of Iowa or Tunisia. The country’s grid correspondingly does not require the wheeling of power over large distances. The longest distance between two points within the nation is 630 km and, yet, 50% of power demand is concentrated in the Dhaka and Chittagong metropolitan areas. As of 2016, the existing network has a total of 9,909 circuit km of transmission lines (400 kV, 230 kV, and 132 kV) connecting 139 substations.<sup>6</sup> One 400 kV line is currently in operation between the Dhaka suburbs of Aminbazar and Meghnaghat with 400 kV substations at each end. The existing grid relies on an extensive network of 230kV lines to transport power over longer distances. The East and West grids, which are

> 5 Domestic prices: Petrobangla. “Annual Report 2014‐15”, 2017; International Prices: IEA. “Energy Prices and Taxes” 2017.

> 6 Power Grid Company of Bangladesh. Source: https://www.pgcb.org.bd/PGCB/?a=pages/substations.php

5
# **5 Conclusions**

This paper confirms international competition as a source of increased returns to skill. Using a large panel (1986-2000) of matched employer-employee data for the Portuguese manufacturing sector, two different skill definitions, and two different identification strategies of exogenous changes in international competition, we show that returns to skill within an industry increase with competition. Furthermore, we identify increasing international competition as a significant determinant of skill upgrading. This result is valid both when workers stay in the same industry and when they move. It indicates that skill acquisition is an important part of the adjustment process to a policy change or shock (exogenous to the wage setting conditions within the country) that changes relative wages, and suggests that policy attention to the consequences of increased competition for human capital accumulation seems merited.

20
# **1 Introduction**

Recent literature on international trade has focused on understanding the determinants of exporting at the firm level, in an environment characterized by heterogeneous firms. Since the seminal paper of Melitz (2003) and following a sharp increase in the availability of access to firm- and plant-level data, many empirical studies have supported an approach that emphasizes two main components to explain why only some firms become exporters: their heterogeneous productivity and sunk costs of entry into foreign markets.

An important feature surrounding this literature is the fact that exporters are typically larger, more productive and pay higher wages, besides other positive characteristics. Therefore, identifying a possible causal relationship between exporting status and productivity has become a topic that has received significant attention in the trade literature. The most prevalent view is that most efficient firms self-selected themselves into the export market (see Roberts and Tybout (1997); Clerides et al. (1998); Bernard and Jensen (2004)). However, De Loecker (2007) and Lileeva and Trefler (2010) find that improving foreign market access may increase the productivity of some exporting plants.

A usual justification for the services of export promotion agencies (EPAs) is the existence of market failures which prevent producers from reaching foreign markets despite the fact that they might have enough productivity to compete abroad. For example, Waugh (2010) demonstrates the existence of “asymmetric frictions” in which developing countries face systematically higher costs for exporting than industrialized ones. Also, Cadot et al. (2011) show evidence of network effects (positive externality) for exporters of similar products to those firms exporting for the same destinations. Furthermore, Eaton et al. (2010) show that the behavior of exporting firms reflects a process of searching and learning in the foreign market, where these firms spend resources on identifying buyers and learn about the potential of their products for that market. A high entry cost for learning about foreign markets and uncertainty due to information frictions can deter potential exporters from doing so (see Allen (2011)).

Therefore, the presence of market failures such as externalities and asymmetric information might support some mechanisms of government-sponsored export promotion as a way to make some additional gains from trade feasible.<sup>1</sup> Although export promotion policies should be taken cautiously<sup>2</sup> , they have been applied in many different countries and a significant amount of

> 1Copeland (2007) provides a critical review on the literature and emphasizes two sources of market failure that could justify this kind of interventions: information spillovers and asymmetries of information.

> 2There are important aspects regarding political economy and efficiency to be taken into consideration.

1
**Figure 1: Cross-Country Comparison of Income Class Poverty Rates**

<!-- Start of picture text --> 2002/3 2014/15<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>international poverty line lower middle income class poverty upper middle income class poverty<br>line line<br>Poverty Headcount<br>Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine<br><!-- End of picture text --> _Source: Official Survey Data by Country, Author’s calculations_

**Kazakhstan** has achieved large and enduring reductions in poverty since independence. The rate of extreme poverty measured at the international poverty line of $1.9-a-day is now largely indistinguishable from zero. Despite setbacks in 2009 and 2015, poverty measured using all common lines indicate substantially improving welfare over the long-term. The most recent data available suggest that the poverty rate in Kazakhstan fell between 2016 and 2017. Measured against the highest poverty line the World Bank uses – which is usually reserved for upper middle-income countries like Kazakhstan – poverty has fallen from about 65 percent in 2011 down to about 8.5 percent in 2017. But many challenges remain, and Kazakhstan is vulnerable to economic shocks that periodically reverse the country’s progress on poverty reduction. The two most notable cases of this were in 2005 and from about 2013 to 2016. During those spells of low growth or contraction, many people fell into poverty (though not often extreme poverty), and only a return to growth in the following years pulled the poverty rate down again. Indeed, measured at the highest poverty line, Kazakhstan has still yet to return to the low rate it achieved in 2013.

Even at the regional level, welfare and resilience to shocks have strong spatial dimensions in Kazakhstan. During the most recent economic downturn starting in late 2014 and continuing into 2015, regions with lower average incomes and histories of higher poverty rates experienced larger increases in poverty in 2015.

10
**Table 9: Regression results with SEC registration dummy variable** Dependent variable: Log of spread-to-benchmark at launch

||(1)|(2)|
|---|---|---|
|VARIABLES|Pre-crisis|Post-crisis|
|IG dummy|-0.335***|-0.022|
||(-3.69)|(-0.15)|
|S&P Rating|0.136***|0.083***|
||(6.19)|(3.20)|
|Log(size)|0.048|0.033|
||(0.94)|(0.70)|
|Years to|0.012***|-0.003|
|Maturity|||
||(3.74)|(-0.96)|
|U.K. Law|0.069|0.057|
||(0.74)|(0.41)|
|VIX|0.013**|0.027***|
||(2.29)|(5.24)|
|GDP growth (_t-1_)|-0.009|-0.010|
||(-1.18)|(-0.83)|
|EAP|0.136|0.019|
||(1.28)|(0.10)|
|ECA|0.141|0.359|
||(1.24)|(1.56)|
|LAC|0.316***|0.079|
||(3.75)|(0.42)|
|SAS|0.366**|0.344***|
||(2.50)|(2.66)|
|SSA|0.458***|0.428**|
||(3.52)|(2.33)|
|SEC registration|0.080|-0.256**|
||(1.38)|(-2.02)|
|Observations|328|154|
|R-squared|0.656|0.684|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

We also examine whether the spread divergence was caused by higher spreads of U.K. law bonds or lower spreads of U.S law bonds. For this we split the sample for the full period between U.K. law and U.S. law bonds and used a dummy for post-crisis years (Table 10). The results indicate that there was no significant change in U.K. law spreads post-crisis. But there was a statistically significant lowering of spreads in the U.S. law sub-sample associated with the post-crisis period.

18
has been a particularly popular and fruitful data set for policy analysis. The studies utilizing these data are too numerous to list here, but a recent selection is Deaton (1987, 1989), Glewwe (1987, 1991), Grootaert (1987, 1990), Van der Gaag and Vijverberg (1989), Kakwani (1990), Kanbur (1990). The CILSS was designed to measure living standards, and by repeating the survey each year, to monitor changes in household welfare over time (see Grootaert, 1986,- for a further discussion of the survey and its content). However, a disturbing feature of the CILSS results is the recorded decline in mean household size noted by Daho (1992) and Coulombe and Demery (1992). According to these authors, this is due to sampling bias in the early years of the survey. If this is indeed the case, there are obvious questions surrounding the robustness of the analyses undertaken with these data.

This paper assesses the seriousness of this sampling bias, and makes the necessary adjustments to correct it. This involves re-weighting the original data (discussed in Section III). Given the important role of the household size variable in many recent welfare and poverty studies, comparisons are then made (in Section IV) of estimates of mean per capita expenditure, computed with and without the correcting weights. This is followed in Section V with an assessment of the implications of these finding for poverty estimates. In Section VI the implications for estimates of basic needs indicators are discussed. But first, in the following section (II), the household size results of the CILSS are described briefly.

# IL **Household Size in the CILSS**

Between 1985 and 1988, household size in CMte d'Ivoire declined from 8.31 in 1985 to 6.33 in 1988 (a fall of 24%) according to the "raw" CILSS results (Daho, 1992; 2
# **Policy Research Working Paper Series**

||Title|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS226|5 The Political Economy of Distress<br>inEast Asian Financial Institutions|Paola Bongini<br>Stijn Claessens<br>Giovanni Ferri|January 2000|R. Vo<br>33722|
|W,EPS22|66<br>The Impact of Adult Deaths on<br>Children's Health in Northwestern<br>Tanzania|Martha Ainsworth<br>Innocent Semali|January 2000|S. Fallon<br>38009|
|WP32267|Do High Interest Rates Defend<br>Currencies during Speculative Attack|Aart Kraay<br>s?|January 2000|R. Bonfield<br>31248|
|WPS226|8 The Structure of Social Disparities<br>In Education: Gender and Wealth|Deon Filmer|January 2000|S. Fallon<br>38009|
|WPS226|9 Context Is Everything: Measuring<br>Institutional Change in Transition<br>Economies|Nauro F. Campos|January 2000|J. Victor<br>36549|
|WPS227|0 The Optimal Income Tax When<br>Poverty Is a Public "Bad"|Waly Wane|January 2000|H. Sladovich<br>37698|
|WVPS227|1Corporate Risk around the World|Stijn Claessens<br>Simeon Djankov<br>Tatiana Nenova|January 2000|R. Vo<br>33722|
|WPS227|2 Ownership versus Environment:<br>Disentangling the Sources of Public<br>Sector Inefficiency|Ann P. Bartel<br>Ann E. Harrison|January 2000|S. Fallon<br>38009|
|WPS227|3 The Value of Preventing Malaria<br>In Tembien, Ethiopia|Maureen L. Cropper<br>Mitiku Haile<br>Julian A. Lampietti<br>Christine Poulos<br>Dale Whittington|January 2000|T. Tourougui<br>87431|
|WPS227|4 How Access to Urban Potable Water<br>and Sewerage Connections Affects<br>Child Mortality|Anqing Shi|January 2000|P. Sintim-Aboagye<br>37644|
|WPS227|5 Who Gained from Vietnam's Boom<br>In the 1990s? An Analysis of Poverty<br>An Analysis of Poverty and<br>lnequality Trends|Paul Glewwe<br>Michele Gragnoiati<br>Hassan Zaman|January 2000|P. Sader<br>33902|
|AIPS227|6 Evaluating the Case for Export<br>Subsidies|Arvind Panagariya|January 2000|L. Tabada<br>36896|
**_Figure 2. Annual TFP growth rate for all, OECD, and developing countries, median and simple average by decade_**

<!-- Start of picture text -->
1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0%<br>-0.2%<br>-0.4%<br>-0.6%<br>-0.8%<br>-1.0%<br>85-94 94-04 04-14 85-94 94-04 04-14 85-94 94-04 04-14<br>All OECD Developing<br>Median Average Median Average Median Average<br>Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data.<br>Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years;<br>the former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.<br>Figure 3. Annual TFP growth rate for developing countries, median and simple average by region and decade<br>3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>East Asia Europe Latin America Middle East South Sub-Saharan<br>and and and and Asia Africa<br>Pacific Central Asia Caribbean North Africa<br>Median Average<br>Annual TFP growth rate<br>Annual TFP growth rate<br><!-- End of picture text -->

**_Figure 3. Annual TFP growth rate for developing countries, median and simple average by region and decade_**

Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data. Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.

20
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# **Broadband Internet and Household Welfare in Senegal**<sup>**1**</sup>

Takaaki Masaki<sup>2</sup> Rogelio Granguillhome Ochoa<sup>3</sup> Carlos Rodríguez-Castelán<sup>4</sup>

**JEL codes:** F63, I31, L86, O12 **Keywords:** Poverty, Household consumption, Mobile broadband, Africa, Senegal

> 1 The authors wish to thank Andrew Dabalen, Mark Dutz, Samantha Lach, Johan Mistiaen, Tobias Pfutze, Robert Zimmerman and peer reviewers of the report Digital Sources of Growth in Senegal for their comments. Thea authors would also like to thank Brian Blankespoor, Arthur Denis Pascal Foch, and Djibril Ndoye for their support accessing data. . The findings, interpretations, and conclusions expressed in this paper 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.

> 2 Economist, Poverty and Equity GP, World Bank (tmasaki@worldbank.org)

> 3 Consultant, Poverty and Equity GP, World Bank (rgranguillhomeoc@worldbank.org)

> 4 Lead Economist, Poverty and Equity GP, World Bank (crodriguezc@worldbank.org)