rafmacalaba/datause-encoder-data huggingface dataset:
# **Account penetration by within-economy income quintile**

Adults with an account at a formal financial institution (%) <!-- Start of picture text --> Middle East & North Africa<br><!-- End of picture text --> <!-- Start of picture text --> RICHEST 25<br>Q4<br>Q3<br>Q2<br>POOREST 7<br>Sub-Saharan Africa<br>45<br>12<br>South Asia<br>51<br>21<br><!-- End of picture text --> Latin America & Caribbean <!-- Start of picture text --> 61<br>21<br>Europe & Central Asia<br><!-- End of picture text --> <!-- Start of picture text --> 58<br>32<br>East Asia & Pacific<br>76<br>33<br>High-income economies<br>91<br>85<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

The difference in length between the bars in figure 1.4—that is, the difference in account penetration between income quintiles—is a rough measure of the gap in financial inclusion between rich and poor people within economies. Because the upper limit is 100 percent, there is little absolute difference in length between the bars for high-income economies, showing that in these economies on average, poorer adults are not significantly less likely than richer adults to have a formal account. But there are stark differences within some developing economies. In both Cameroon and Nigeria about 13 percent of adults in the poorest quintile have an account. Yet while only 22 percent of those in the richest quintile have an account in Cameroon, 62 percent do in Nigeria.

There is a strong correlation between inequality in the use of formal accounts and general income inequality as measured by the Gini coefficient (with higher values indicating higher income inequality). The contrasting situations in two countries illustrate. In Sweden, which has one of the lowest Gini coefficients (25), account penetration in the poorest income quintile is essentially the same as in the richest (resulting in a value of close to 1 on the _y_ -axis of figure 1.5). In Paraguay, at the other end of the spectrum with a Gini coefficient of 52, there is a large gap in account penetration: only 4 percent of adults in the poorest quintile have a formal account, compared with 51 percent in the richest (resulting in a value of about 13 on the _y_ -axis).

The correlation between these two measures of financial and economic inequality (0.42) shows a strong relationship, which holds even when controlling for national income. But it also suggests that there are factors beyond income inequality that explain the large variation in the use of formal accounts. Consider the example of the United Kingdom and the United States (figure 1.6). These two countries have relatively similar Gini coefficients and relatively similar account penetration among adults in the top four income quintiles (92 percent in the United States

**14**

MEASURING FINANCIAL INCLUSION
from deregulation of services, good institutions are needed to guide this process of services liberalization (Beverelli, et al., 2015; Van der Marel, 2016).

**This evidence suggests that appropriate regulation of services facilitates productivity growth in manufacturing and strengthens GVC’s which in turn increases the competitiveness of countries.**

# **2. The Role of Services in the Turkish Economy**

**Productivity in Turkey has stagnated in recent years.** Total factor productivity (TFP) is one of the most important long‐term determinants of growth.<sup>4</sup> Although Turkey has seen impressive GDP growth achievements in the last several decades as it approaches high‐income status, average TFP growth has been negligible since 2000. Moreover, TFP seems to be trending downward more recently (left‐hand panel of Figure 2.1). Although ailing TFP growth is a worldwide phenomenon (Adler, 2017; Eichengreen et al., 2015), particularly after the global financial crisis, it is of concern for Turkey given that several comparator countries, such as China, India, the Russian Federation, and Poland had substantially higher TFP growth during this period (right‐hand panel of Figure 2.1). _Given that services account for over half of GDP in Turkey, it is important to understand the performance of the services sector and how it can become an engine for future productivity growth._

**Figure 2.1: Productivity (TFP) performance of Turkey and comparators (2000‐2014)**

<!-- Start of picture text --> TFP TFP growth 2000-2014<br>.8<br>0.7<br>.6<br>.4<br>0.4 0.4<br>0.3<br>.2<br>0.1<br>0 0.1 - 0.0<br>0.0 -0.1<br>-.2 -0.2 -0.3<br>2000 2002 2004 2006 2008 2010 2012 2014 RUS IND CHN POL CZE IDN TUR HUN BRA BGR ZAF<br>1.5<br>1.25<br>1<br>.75<br>.5<br><!-- End of picture text --> Source: Penn World Tables; author’s calculations. Note: the TFP numbers from the Penn World Tables may not reflect the most recent GDP figures for Turkey, since they were recently revised by the government.

**Productivity in services seems to be low for Turkey, especially compared to manufacturing.** Although productivity, and particularly TFP, is hard to compute for services, a rough indication of labor productivity is possible by looking at revenue per worker (left‐hand panel of Figure 2.2), as well as the ratio of revenue to the wage bill (right‐hand panel of Figure 2.2), for services firms.<sup>5</sup> Turkey performs below most

> 4 TFP refers to the portion of output not explained by the amount of inputs used in production, including capital and labor. It measures how efficient capital and labor are used with the help of new technologies.

> 5 Due to the lack of variables such as value‐added to compute labor productivity or TFP, revenue per worker serves as a crude indicator of productivity. Usually this measure is correlated with labor productivity or TFP.

6
Table 1: Characteristics of firms with female versus male managers

|||All||M|So<br>ore tradi|cial perceptio<br>tional|ns about w<br>L|omen:<br>ess tradit|ional|
|---|---|---|---|---|---|---|---|---|---|
||Female|Male|Mean dif.<br>test|Female|Male|Mean dif.<br>test|Female|Male|Mean dif.<br>test|
|Manager experience|18.55|20.70|-2.149***|17.56|19.34|-1.788***|19.53|22.84|-3.311***|
|Firm age: < 10 years|0.25|0.22|.029***|0.31|0.24|.068***|0.20|0.20|0.003|
|<br>Firm age: Between 10 and 19 years|0.36|0.34|0.012|0.40|0.37|.033***|0.31|0.30|0.007|
|<br>Firm age: 20 years or more|0.39|0.43|-.042***|0.29|0.39|-.1***|0.49|0.50|-0.01|
|Firm size: Less than 20 FT workers|0.61|0.57|.035***|0.60|0.56|.042***|0.62|0.60|0.017|
|Firm size: Between 20 and 99 FT workers|0.31|0.33|-.019**|0.30|0.34|-.041***|0.32|0.32|0.009|
|Firm size: 100 FT workers or more|0.08|0.09|-.016***|0.10|0.10|-0.001|0.05|0.08|-.026***|
|Sector: Low tech intensity|0.76|0.60|.153***|0.77|0.63|.146***|0.74|0.56|.177***|
|Sector: Medium tech intensity|0.14|0.25|-.114***|0.13|0.25|-.118***|0.15|0.27|-.113***|
|Sector: High tech intensity|0.10|0.14|-.04***|0.10|0.13|-.028***|0.11|0.17|-.063***|
|Exporting frm|0.33|0.29|.036***|0.27|0.22|.049***|0.38|0.40|-0.019|
|<br>Firm received audit|0.40|0.43|-.03***|0.44|0.44|-0.002|0.36|0.41|-.051***|
|Credit application|0.20|0.14|.06***|0.15|0.09|.059***|0.25|0.22|.032**|
|<br>Credit rejection|0.10|0.13|-.029*|0.17|0.17|0.007|0.05|0.10|-.047**|
|<br>Credit constraint|0.28|0.34|-.063***|0.35|0.39|-.04***|0.21|0.27|-.057***|
|Has an open line of credit|0.28|0.20|.083***|0.17|0.11|.052***|0.40|0.34|.061***|
|Type of institution that granted credit: private bank|0.84|0.82|0.02|0.71|0.71|-0.01|0.89|0.88|0.01|
|Type of institution that granted credit: state own bank|0.13|0.13|-0.01|0.28|0.21|.069**|0.06|0.09|-.029*|
|Type of institution that granted credit: non-bank fnancial|0.03|0.04|-0.01|0.01|0.07|-.056***|0.04|0.03|0.01|
|Type of institution that granted credit: other|0.01|0.01|0.00|0.00|0.01|-0.01|0.01|0.01|0.00|
|Log of value of debt|10.75|11.21|-.456***|9.70|10.80|-1.104***|11.18|11.42|-.241**|
|<br>Leverage|0.01|0.00|0.007|-0.01|0.00|-0.017|0.03|-0.01|0.031|
|Profts/Revenue|0.14|-0.01|.15***|0.12|-0.01|.131***|0.16|-0.01|.17***|
|TFP|0.01|0.00|0.01|0.03|0.00|0.029|-0.01|0.00|-0.01|
|Log of sales per worker|-0.39|-0.16|-.233***|-0.31|-0.10|-.207***|-0.46|-0.24|-.227***|
|<br>Avg. return to capital|1.18|1.00|.188***|1.23|1.01|.214***|1.14|0.97|.172***|
|Capital distortion|0.80|0.75|0.049|0.80|0.69|0.114|0.80|0.85|-0.051|

Sources: World Bank Enterprise Surveys, World Value Surveys and World Development Indicators. Notes: Calculations using survey weights. See Table A.1 in the Appendix for details on variables definition and section 2 for details on the classification of countries as More or Less traditional.
Policy Research Working Paper 7287

# **Abstract**

The empirical literature on institutions and development has been challenged on grounds of reverse causality, measurement error in institutional indicators, and heterogeneity. This paper uses firm-level data across countries to confront these challenges. Instead of analyzing ultimate outcomes, such as income levels where institutional quality is likely endogenous, the focus is on firm-level external finance. Moreover, institutions are “unbundled” to explore how various types of institutions affect external finance differently. The paper documents that micro firms have significantly less access to external finance than small and medium firms. General financial development and contracting institutions that facilitate transactions between private parties exert little effect, on average, on firms’ access to external finance. In contrast, property rights institutions that constrain political and economic elites exhibit stronger positive association with access to external finance. The analysis finds evidence of attenuation bias associated with error in measuring institutions. For leveling the playing field between elite and non-elite firms (as proxied by firm size) in their access to external finance, property rights institutions also figure more prominently—with an important exception for the information environment, a component of contracting institutions. The results indicate that a specific channel through which development is affected more by property rights institutions rather than contracting institutions is external financing for firms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at lxu1@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
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endorsing its predictions, at least qualitatively.<sup>3</sup> The model predicts protection to be a simple function of the existence of lobbying organization, the inverse of the import-to-output and export-to-output ratios and the import demand and export supply elasticities. The tightness of its theoretical predictions make the model amenable to testing using cross-sectional data. We apply the Grossman-Helpman model to the case of US agricultural trade policy, using precise measures of lobbying organization and agricultural protection and subsidies to a cross section of agricultural commodities from the late 1990s. To our knowledge this is the first study to use agriculture data to test the Grossman-Helpman model. It is also the first to seriously consider subsidies within the Grossman-Helpman framework.

We go beyond previous empirical studies of the Grossman-Helpman model as well. We would expect a government that is influenced by contributions to trade off consumer welfare loss for contribution dollars on an about equal basis. However, previous studies indicate that the US government is actually a welfare lover and places almost negligible weight on contributions. This paper suggests a novel resolution for that puzzle. We show that it is the consequence of taking a feature of the theory all too literally. Doing away with the “unitary” government assumption and introducing policy uncertainty, that is, making the model more real-worldly, rids the paradox.

The paper proceeds as follows. Section 2 briefly surveys the literature. It emphasizes the difference between the reduced form methods traditionally used in the agriculture protection literature and the structural method implemented in this paper. In Section 3 the GrossmanHelpman model is introduced and its main predictions stated and described. The data are described in Section 4. The data are new and theoretically appropriate for testing the model. In Section 5 the model’s prediction is tested and the results analyzed. A puzzle posed by this and previous empirical work in this area is resolved. Section 6 concludes.

> 3 Eicher and Osang 2002, Gawande and Bandyopadhyay (2000), Goldberg and Maggi 1999, Mitra, Thomakos, and Ulubasoglu 2002, and McCalman 2004. See survey by Gawande and Krishna (2004).

2
Figure 8-A

# Real Wage and Real Exchange Rate

<!-- Start of picture text --> AUCWrCISA<br>'IiS 'L2<br><!-- End of picture text --> U. - -~~~~~~~~~~~~~~~~~~~~~~Cnwr <!-- Start of picture text --> ama<br>so~~~~~~~~~~~~~~~~~~~~~o<br><!-- End of picture text --> <!-- Start of picture text --> HU'AZIS.<br>1:3 _rL _["^G^4tU- UB4X<br>/ c<br>Ml=of aU 3i l tmsuc .CtU TO= c 3X<br><!-- End of picture text --> _urnsMC_ <!-- Start of picture text --> ISO<br>Ian~aamur<br>m:00<br>110~~~~~~~~~~~~~~~~~!.<br><!-- End of picture text -->
# **1 Introduction**

There is growing emphasis on investing oil revenues domestically in oil-rich countries. During the oil price boom in 2000s, many oil rich countries began to break away from their traditional investment strategies, which focused on channeling the money back into financial systems of the advanced economies, and undertook ambitious domestic investment programs.<sup>1</sup> Despite the sharp decline in oil prices in recent years, the trend towards domestic investments seems to have gained momentum with anemic recovery and ever increasing economic and financial uncertanties in the advanced markets. For instance, the officials in the Kingdom of Saudi Arabia have recently announced publicly selling shares of the state oil giant, Saudi Aramco, and routing much of its worth, an estimated $ 2 trillion, into a public investment fund.<sup>2</sup> With persistently low interest rates in the aftermath of the global financial crisis, it may seem obvious that some of those domestic projects which were deemed not desirable before may become attractive. Notwithstanding the immediate appeal of such arguments, however, is the fact that policy makers, who are to act on behalf of all constituents in their jurisdictions, typically operate with complex objectives. Successful implementation of public investments are bounded with both the availability of projects with good returns and the capacity of authorities to manage them.<sup>3</sup> In addition, fiscal solvency and sustainability constraints may prevent governments from incurring large deficits and accumulating excessive public debt. Last, but not least, policy makers are also concerned with the distribution of wealth across generations. Thus, facing different implementation constraints, resource horizons, and initial conditions, the desired scale and pace of such investments may be determined differently across different economies.

In this paper, we compare alternative public investment paths in terms of their impact on growth in the non-oil sector and fiscal outcomes. Central to the conduct of fiscal policy is a resource fund that receives inflows from revenue from the taxation of oil profits and interest payments from the accumulation of assets. Public investment is part of the

> 1See Abdelal _et al._ (2008) for an analysis of this shift in investment strategies in the context of the Gulf Cooperation Council (GCC).

> 2See http://www.bloomberg.com/news/articles/2016-04-01/saudi-arabia-plans-2-trillion-megafund-todwarf-all-its-rivals

> 3See Albino-War _et al._ (2014) for an analysis on the importance of public investment management in oil-rich countries.

2
|Pamir Private Power Project|Imtiaz Hizkil|
|---|---|
|Pamir Private Power Project|Imtiaz Hizkil|
|Lao PDR First PovertyReduction Support Credit|Alessandro Magnoli Bocchi|
|Lao PDR First PovertyReduction Support Credit|Alessandro Magnoli Bocchi|
|Lao PDR First PovertyReduction Support Credit|Alessandro Magnoli Bocchi|
|THIRD COMMUNITY EMPOWERMENT AND LOCAL GOVERNANCE|PRScott E. Guggenheim|
|THIRD COMMUNITY EMPOWERMENT AND LOCAL GOVERNANCE|PRScott E. Guggenheim|
|THIRD COMMUNITY EMPOWERMENT AND LOCAL GOVERNANCE|PRScott E. Guggenheim|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|EnergyEfficiencyProject|Arturo S. Rivera|
|PovertyReduction Support Credit(2)|Nicola Pontara|
|PovertyReduction Support Credit(2)|Nicola Pontara|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|Rio de Janeiro Sustainable Integrated Ecosystem Management in|Pr Marianne Grosclaude|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|e-LearningSupport Project(APL #1)|Isak Froumin|
|PovertyReduction Support Credit II|Martin G. Rama|
|PovertyReduction Support Credit II|Martin G. Rama|
|PovertyReduction Support Credit II|Martin G. Rama|
|Public Financial Management Reform Project|Quyen HoangVu|
islands), and diseconomies of scale (due to the prevalence of small national systems). However, in principle, these costs could come down in the future as countries harness more cost-effective sources of energy and exploit regional power trade to expand the scale of production.

LRMCs are calculated using a dynamic model that estimates the needs of African power systems based on economic growth projections and electrification targets. The model simulates optimal (least-cost) strategies for generating, transmitting, and distributing electricity in response to demand increases. It also estimates the cost of meeting power demand under a range of alternative scenarios, including cross-border trade (Vennemo and Rosnes 2008).<sup>7</sup> With few exceptions, a more efficient selection of technologies (with and without greater regional trade) would render LRMCs below 10 cents/kWh (figure 15). Only four countries—Burkina Faso, Mali, Niger, and Senegal—would face LRMCs over 20 cents/kWh.

Developing African power systems with a view toward expanding regional trade clearly reduces LRMCs across most countries. In fact, if regional trade were fully pursued countries would see their LRMCs reduced by about 10 percent on average, and as high as 40 percent in some cases (figure 16a). These would translate into LRMC reductions of as high as 7 cents/kWh, but more typically in the 1–5 cents range (figure 16b).

# Figure 15. Long-run marginal costs (US cents per kWh)

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Current patterns of trade Trade expansion<br>Congo (DRC) Mozambique Malawi Angola Botswana Congo Lesotho South Africa Cameroon Chad Gabon Guinea Equatorial Guinea Gambia Liberia Zambia Zimbabwe Guinea-Bissau Sierra Leone Ghana Tanzania Togo Burundi Central African Rep. Namibia Kenya Rwanda Uganda Nigeria Sudan Cote d'Ivoire Benin Ethiopia Burkina Faso Mali Niger Senegal<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008

> 7 For a brief description of the model, see annex 10.

26