rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Sub-Saharan Africa has been experiencing a growth spurt with high growth rates in gross domestic product (GDP) and modest increases in GDP per capita since the mid1990s (See Figure 1). The growth and overall economic performance of Sub-Saharan Africa (SSA) in the 1970s and 1980s was disappointing for various reasons and has often been described in negative terms such as a “growth tragedy”.<sup>1</sup> After sluggish or negative growth in GDP per capita during the “lost decades,” the region has been rapidly regaining its vitality. That the global recession in 2008 and 2009 carried little impact on SSA economies adds optimism for the region’s growth prospects.<sup>2</sup> Such optimistic views are reflected in the positive tone and rhetoric in recent publications such as “Lion on the Move,” “The Sun Shines Bright,” and “African Growth Miracle.”<sup>3</sup> Recent reports cautiously predict continued growth.<sup>4</sup> A key question follows: What accounts for the turnaround of the growth performance in the mid-1990s? Answering this question can help explain whether the economic spurt is a temporary boom or the beginning of a sustainable takeoff. This is not to foretell the future of SSA, but to inform policy makers regarding what needs to be further promoted to make the economic environment conducive to sustainable development.

Mixed views on the growth prospect of SSA countries exist. A common optimistic view on SSA’s economic takeoff stresses improved human development such as fertility decline, and better health and education. An improved political and business environment with the help of reduced conflicts is considered to be a contributing factor. In contrast, many existing studies that explain African growth (or lack of) tend to weigh on a pessimistic view. They often highlight features pertinent to the continent such as: Africa’s colonial past, conflicted history, and geography<sup>5</sup> ;

> 1 See Easterly and Levine (1997).

> 2 Although the global economic downturn of 2008 and 2009 hit Africa with a lag, the policy response to the crisis in Africa is deemed to be appropriate and arguably helped avoid a deeper recession. For example, see Dejaravan  and Kasekende (2011).

> 3 McKinsey Global Institute (2010) for “Lion on the Move,” _The Economist_ (December 3, 2011) for “The Sun Shines Bright,” and Young (2012) for “African Growth Miracle.”

> 4 See International Monetary Fund (2012).

> 5 See Sachs and Warner (1997).

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WPS3431

# **FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY, AND GROWTH**<sup>*****</sup>

Norman Loayza World Bank Romain Rancière CREI and Universitat Pompeu Fabra

## October 2004

### **Abstract**

This paper studies the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity. On the one hand, the empirical growth literature finds a positive effect of financial depth as measured by, for instance, private domestic credit and liquid liabilities (e.g., Levine, Loayza, and Beck 2000). On the other hand, the banking and currency crisis literature finds that monetary aggregates, such as domestic credit, are among the best predictors of crises and their related economic downturns (e.g., Kaminski and Reinhart 1999). The paper accounts for these contrasting effects based on the distinction between the short- and long-run impacts of financial intermediation. Working with a panel of cross-country and time-series observations, the paper estimates an encompassing model of short- and long-run effects using the Pooled Mean Group estimator developed by Pesaran, Shin, and Smith (1999). The conclusion from this analysis is that a positive long-run relationship between financial intermediation and output growth co-exists with a mostly negative short-run relationship. The paper further develops an explanation for these contrasting effects by relating them to recent theoretical models, by linking the estimated short-run effects to measures of financial fragility (namely, banking crises and financial volatility), and by jointly analyzing the effects of financial depth and fragility in classic panel growth regressions.

> *   This paper was written while Loayza and Rancière worked in the research department of the Central Bank of Chile.  Research assistance from Megumi Kubota and Guillermo Vuletin is gratefully acknowledged.  We benefited from comments and advice by J. Benhabib, S. Bond, J. Cummings, F. Gallego, A. Gaytán, P.O. Gourinchas, O. Jeanne, B. Jovanovic, P. Martin, D. Ray, L. Reichlin, K. SchmidtHebbel, M. Spiegel, A. Rose, A. Tornell, R. Soto, Paul Evans –the journal editor--, two anonymous referees, and seminar participants at New York University, Stanford University, University of California at Irvine, the Bank of England, the Central Bank of Chile, the Royal Bank of Sweden, the 2001 CESifo Summer Institute in Venice, and the 2001 Meetings of the Latin American and Caribbean Economic Association in Montevideo.  The usual disclaimer applies.  Correspondence: nloayza@worldbank.org.
# PRODUCTIVITY AND EXTERNJALITIES:

# MODELS (F EXPORT-LED GROWTrH

# Table of Contents

|I.|Introduction<br>..|1.|
|---|---|---|
|I.|Export-Led Growth: The Evidence<br>........................ .|2|
|Im.|Externalities and Growth<br>........................................|_5_|
|_rV._|A Model with an Export Externality ..................<br>.|9|
|V.|A Model wa", Import and Export Externalities .13||
|VI.|Illustrative Simulations of ELG Industrialization .....................|.<br>17|
|VII.|Conclusion ..................................................|20|
|Refe|rences .......................................................|22|
|App|endix ........................................................|26|

This paper was prepared under funding from the World Bank research projec4 "Industrial Competition, Productive Efficiency, and Their Re! tdon to Trade Regimese (RPO 674-46). We thank Bela Balassa, Shantayanan Devarajan, Riccardo Faini, Ram6n E. L6pez, Arvind Panagariya, and James Tybout for comments and also parti pants at seminars at the University of California, Berkeley; MIT; Yale University; and IAsA.
robustness of our results in two ways. First we restricted the sample to 1998 through 2006 and redid our estimates. Second, we used 1998-2007 but excluded the 26 middle cities. The results are robust to both these extensions.

We also reproduced the specifications employed by Nie, Fang, and Li (2010) in Appendix Tables 1 through 3. In order to see why we get different results, we use their variable definitions, with investment defined as annual growth in fixed assets, and the same control variables. The dependent variable is the mean post 2004 – the mean before 2004 and the independent variables are the means before 2004. According to Appendix Table 1 columns (1) - (3), using the same years 1999-2003 and 2005, we can get the same sign and similar magnitudes of effects as Nie, Fang, and Li (2010). However, if we extend the sample to 1998-2007 and do the same estimation, the results reported in columns (4) - (6) show that the effect on fixed investment is much smaller and becomes insignificant. There are other problems with their approach in addition to lack of robustness over the longer time horizon. For example, they do not address the potential endogeneity of treatment, and their definition of investment is problematic too (the value of fixed assets is measured in nominal terms and depreciation is not taken into account).

Appendix Tables A.2 and A.3 separate the results into SOEs and non-SOEs. The results in Appendix Table A.2 show that the results in Nie et al are driven by the changes in fixed investment across SOEs. Comparing the first three and last three columns, which include 2006 and 2007, we see that the significant effects on investment were only present in 2005, and disappear if we add 2006 and 2007. Appendix Table A.3 reports the results for non-SOEs, for which the impact on fixed investment was negative. Overall, these additional tables confirm that the positive impact on investment was short-lived and 20
_Africa’s Macroeconomic Story_ Growth in African countries has been more episodic than in other developing regions, as described by Page (2009): ‐ _“Africa’s long run record of slow and volatile growth reflects a pattern of offsetting accelerations and declines, rather than random variations of growth rates around the long_ ‐ _run trend.” (p. 2)_ Much of the empirical literature has therefore focused on the episodic nature of growth, beginning with a paper by Hausmann et al. (2005) that examines “growth accelerations”, defined as episodes where higher growth is sustained for more than 8 years. Their analysis of 83 such episodes, 20 of which involve African countries, indicates that: _“growth accelerations are not proceeded or accompanied by major changes in economic policies, institutional arrangements, political circumstances, or external conditions.” (p. 328)_ An IMF (2008) study pursues a similar methodology focusing specifically on episodes in Africa since 1995 and comes to a similar conclusion: _“the growth takeoff in sub-Saharan Africa since 1995 has been associated with several factors, and no simple story emerges” (p. 41)_ characterizing macroeconomic stability as a necessary and but sufficient condition: _“a number of conditions that are apparently necessary for sustained takeoff: absence of conflict; a stable macroeconomy, as reflected in low inflation and sustainable debt and a functioning market economy... A clear set of sufficient conditions for rapid growth does not emerge. The fast growers have, on the whole, done better in the areas of building reserves, sustaining high levels of public investment, and avoiding overvaluation of the real exchange rate, and on some measures of broad policy reform, notably the World Bank’s CPIA (the latter two hold true mainly for non-resource-rich fast growers). However, these patterns are not stark. In general, growth seems to have responded to the specific circumstances in each country in terms of the external environment it faced and the policy choices it made.” (p. 42)_ Research along the same lines by Arbache and Page (2009) finds that growth accelerations over the post-1995 period were more prevalent in resource-rich countries: _the “improvement in economic performance in Africa after 1995 turns out to be due largely to two factors: a substantial reduction in the frequency and severity of growth declines in all economies and an increase in growth accelerations in mineral-rich economies”._ 5
Table 1: Dependent Variables

|**Variable**|**Acronym**|**Unit**|**Expected Impact of**<br>**Renewable Energy Policies**|
|---|---|---|---|
|Fossil fuel energy (oil, gas & coal)<br>consumption|FCC|Percent of total|Negative|
|Electricity production from fossil<br>fuel(oil,gas & coal)sources|EFF|Percent of total electricity<br>output|Negative|
|Electricity production from oil<br>sources|EOS|Percent of total electricity<br>output|Negative|
|Renewable energy consumption|REC|Percent of total final<br>energyconsumption|Positive|
|Renewable electricity output|REO|Percent of total electricity<br>output|Positive|

_Source: World Bank World Development Indicators (WDI) database, 2020._

# 3.2 EXPLANATORY VARIABLES

Our explanatory variables come from the 2018 disaggregated policy instrument data behind the Regulatory Indicators for Sustainable Energy (RISE) renewable energy "traffic light" indicators that the Energy Sector Management Assistance Program (ESMAP) at the World Bank has published since 2010. Concretely, our independent variables are indexes that represent seven energy policy packages. Each energy policy package comprises the disaggregated policy instruments in the RISE data. In this section, we describe the primary RISE dataset and the three alternative methods we designed to reduce the dimensionality of the dataset and create our independent variables indexes.

In the primary RISE dataset, each policy is coded in two variables: one indicating whether the policy exists through yes/no answer, and the second specifying the year of the first instance of the policy, if applicable.<sup>3</sup> When restructuring the dataset, we combine the two variables for each policy instrument into one. Its value changed from 0 to 1 when the first policy was put into place, creating the panel dataset needed for the rest of the analysis.

> 3 For instance, "Does a legal framework for renewable energy development exist?" and the year for the first legal framework.

11
# **Destination by Type of Non-fuel Export**

<!-- Start of picture text --> Other<br>EU28<br>Machinery & Transport Rest of Asia<br>Non‐CIU CIS<br>Metals<br>Kazakhstan<br>Wood Rest of Europe<br>(incl. Turkey)<br>China<br>Chemicals<br>Belarus<br>Minerals (excl. mineral<br>fuels) MENA<br>North America<br>Food & Agriculture<br>ROW<br>0% 20% 40% 60% 80% 100%<br><!-- End of picture text --> _Source: COMTRADE. Notes: MENA = Middle East and North Africa; nonCU-CIS = members of the Commonwealth of Independent States, except the original three members of EACU; ROW = Rest of World; EU = European Union._ 0
Figure 2.5: Margin Decomposition for Retail (BRL million) Figure 2.6: Margin Decomposition for Corporate (BRL million) <!-- Start of picture text --> Lending Lending<br>87.2 16.8<br>Margin* Margin*<br>Borrowing Borrowing<br>Margin* 7.5 Margin* 11.4<br>Interest Interest<br>94.7 28.2<br>Margin Margin<br><!-- End of picture text --> _* The Lending Margin is the difference between loan-related interest revenues and the cost of funding them (interbank rate for free lending and relevant ‘tied funding’ rate for directed lending). The Borrowing Margin is the difference between interest income from deposits (both actual income from reserve requirements and ‘notional’ income from investing freely available deposits at the interbank rate) and interest expenses. The Interest Margin is the sum of these two margins (i.e. net interest income). Source: Study calculations based on Bacen data._ 14. **The Retail business is significantly costlier to operate than Corporate** . This stems both from high operational costs (as shown by higher cost-to-income and operational expenses/total assets ratios) and from credit expenses (Retail accounts for almost two-thirds of total loan loss provisions). The former are associated with the required supporting infrastructure (i.e., the branch network and information technology) while the latter can be attributed to the relatively higher average default and loss experience of Retail clients, itself partly due to insufficient credit history and lack of positive information sharing (see next section for a discussion).

15. **Even after controlling for risks and expenses, the Retail business line generates higher profits than Corporate because its high margins and fees more than compensate higher operating costs.** This is due to the aforementioned higher spreads, in particular the lending margin. Higher profits also stem from the much greater participation of the Retail business line in non-interest income, which comprises fees generated by loans, payments and other banking services. As a result, Retail generates around half of the total banking system’s pre-tax income. By contrast, even though the Corporate business line is responsible for less than 15 percent of operating costs and one third of credit expenses, it only generates less than 20 percent of the total pre-tax income of the banking system (see Figures 2.7 and 2.8).

13
Table C6: Urban and poverty status changes

|**(A) Original defn**|**itions to DOU**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|---|---|---|---|---|---|---|---|---|---|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|88.8|11.2|0.0|0.0|87.8|11.2|0.9|0.0|
||Rural|33.6|66.4|0.0|0.0|31.5|65.8|1.0|1.6|
|Poor|Urban|0.0|0.0|77.3|22.7|4.9|2.5|74.7|17.8|
||Rural|0.0|0.0|17.1|82.9|0.1|3.1|17.5|79.3|
|**(B) Original defn**|**itions to DB**|**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DOU:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**Ofcial urban def**|**nition:**|||||||||
|Non-poor|Urban|89.0|4.5|0.0|6.5|93.1|6.5|0.4|0.0|
||Rural|46.7|20.0|0.0|33.2|64.6|32.2|2.5|0.7|
|Poor|Urban|0.0|0.0|91.6|8.4|6.5|1.0|85.9|6.6|
||Rural|0.0|0.0|54.7|45.3|1.4|2.3|63.9|32.4|
|**(C) DOU to DB**||**Non**|**-spatia**|**lly defa**|**ted**|**S**|**patially **|**defate**|**d**|
|**Poverty status:**||Non-|poor|Po|or|Non-|poor|Po|or|
|**DB:**||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|**DOU:**||||||||||
|Non-poor|Urban|90.9|2.6|0.0|6.5|93.1|6.6|0.3|0.0|
||Rural|29.4|27.9|0.0|42.7|56.0|41.4|2.0|0.6|
|Poor|Urban|0.0|0.0|96.6|3.4|6.9|0.3|90.1|2.8|
||Rural|0.0|0.0|43.9|56.1|1.1|2.2|58.3|38.5|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line. 18 countries are included. Bangladesh is an outlier and is not included. In Panel (A), welfare is deflated using official spatial deflators. In Panel (B) and (C), welfare is deflated using our updated spatial deflators.

87
While rural and urban stunting rates in Bangladesh have declined markedly over the last two decades (Headey et al. 2015), the stunting rate stood at 36% for the country, and at 31% for urban areas, based on the 2014 Bangladesh Demographic and Health Survey (DHS) (NIPORT et al. 2016). The country’s national stunting rate ranks 107 out of 132 countries (IFPRI 2016). Based on the 2013 BUHS, the stunting rate stood at 50% for slum areas and 33% for nonslum areas in city corporations, and 37% for municipal cities and large towns (NIPORT et al. 2015).

Bangladesh lacks rigorous empirical research based on representative data on the determinants of child growth across, and within, urban areas. A recent, notable exception is Ahsan et al. (2017), who examine the effects of various factors on stunting and underweight status for slum and nonslum children using 2013 BUHS data.<sup>7,8</sup> They find that the child’s age, mother’s education attainment, and household wealth are significant factors for slum and nonslum children, and that mother’s membership in an NGO and the child’s birth order are additional significant factors for nonslum children.

We begin by investigating the effects of child, maternal, household, and neighborhoodarea factors on growth for slum and nonslum children in reduced-form estimations, using the 2013 BUHS. The examined factors proxy for many of the structural and intermediate determinants in the World Health Organization (WHO) Commission on Social Determinants of Health (CSDH) conceptual framework (WHO 2010), and for the immediate, underlying, and basic factors in the United Nations Children’s Fund (UNICEF) conceptual framework on the causes of malnutrition (UNICEF 1998). We estimate regressions with administrative-division fixed effects, as well as regressions with neighborhood-area fixed effects, thus respectively accounting for unobserved heterogeneity at the division and neighborhood-area levels.

We then advance on the Ahsan et al. study in five potentially policy-relevant directions. The first four of the five directions use the 2013 BUHS, whereas the fifth direction uses both the 2006 and 2013 BUHS. First, we examine the effects of the local availability of maternal and child health services, and of the use of these services. Second, we examine the effects of access to, or use of, potential health-protective household amenities related to cooking fuel, dwelling

> 7 For each outcome and sample, they examine the effects of child and maternal factors, household factors, and neighborhood-area factors through separate regression specifications, and consolidate significant factors from these partially-specified regressions into a single regression.

> 8 Other available studies examine the determinants of undernutrition in small samples of children in selected urban slum settlements (for example, see Fakir and Khan 2015; Zaman et al. 2015; Alam et al. 2011).

5
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# Bibliogranhy

- Aitken, N.D., 1973, "The Effect of the EEC and EFTA on European Trade: A Temporal Cross-Section Analysis," American Economic Review, v. 63, #5, pp. 881-92.

- Azam, J.P., 1990, "Informal Integration Through Parallel Markets for Goods and Foreign Exchange", in The Long Term Perspective Study of Sub-Saharan Africa, vol. 4, the World Bank, Washington, D.C.

- Anderson, J., 1979, "A Theoretical Foundation for the Gravity Model," American Economic Review, v. 63 (December), pp. 881-892.

Balassa, B. and M. Noland, 1988, Ja2an in the World Economy, Institute for International Economics.

- Barad, R., 1990, "Unrecorded Transborder Trade and its Implications for Regional Economic Integration, in The Long Term Perspective Study of Sub-Saharan Africa, op. cit.

- Berg, Elliot, et al., 1988, "Regional and Economic Development in Sub-Saharan Africa", a study prepared for USAID, Alexandria, Virginia.

- Bergstand, J., 1989, "The Generalized Gravity Equation, Monopolistic Competition, and the Factorproportions Theory in International Trade", Review of Economics and Statistics, March, pp. 143153.

- Corden, W.M., 1972, "Economies of Scale and Customs Union Theory", Journal of Political Economy, v. 80, 465-75.

- Cooper, C.A. and B.F. Massell, 1965, "Towards a General Theory of Customs Union for Developing Countries", Journal of Political Economy, v. 73, August, pp. 461-75.

- Deardorff, A.V., 1984, "Testing Trade Theories and Predicting Trade Flows," in R.W. Jones and P.B. Kenen (eds): Handbook of International Economics, v. I, Elsevier Science, Amsterdam.

- Deardorff, A.V. and W.F. Stolper, 1990, "Effects of Smuggling under African Conditions: A Factual, Institutional and Analytic Discussion", Weltwirtschaftliches, v. 126, #1, pp. 116-141.

- Diouf, M., 1990, "Evaluation of West African Experiment with Economic Integration", in The Long Term Perspective Study of Sub-Saharan Africa, op. cit.

- Foroutan, F., 1992, "Economic Integration in Sub-Saharan Africa: Experience and Prospects", Policy Research Working Paper, WPS 992, the World Bank, Washington, D.C.

- Frimpong-Ansah, J.H., 1990, "The Prospects for Monetary Union in ECOWAS", in The Long Term Perspective Study, op. cit.

- Geraci, V. and W. Prewo, 1977, "Bilateral Trade Flows and 1 ransport Costs," Review of Economic and Statistics, February, pp. 67-74.
(International Bank for Reconstruction and Development, 2013).

Roger Myerson, "Federalism and incentives for success of democracy," _Quarterly Journal of Political Science_ 1(1):3-23 (2006).

- Roger Myerson, "Toward a theory of leadership and state building," _Proceedings of the National_

- _Academy of Sciences USA_ 108(supplement 4):21297-21301 (2011).

- Roger Myerson, "Democratic Decentralization and Economic Development", to appear in

- _Oxford Handbook of Africa and Economics: Context and Concpets_ , edited by Justin Yifu Lin and Célestin Monga (2014).

- Thomas J. Sargent, "United States Then, Europe Now," _Journal of Political Economy_ 120(1):140 (2012).

- United Cities and Local Governments, _Local Governments in the World: Basic Facts on 82 Selected Countries_ (2007).

http://www.cities-localgovernments.org/gold/upload/pocketbook_complete.pdf 12