rafmacalaba/datause-encoder-data huggingface dataset:
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34
# **I. Introduction**

The Nigerian banking sector has experienced a boom-and-bust cycle in the past 20 years. After Nigeria implemented the Structural Adjustment Program (SAP) in 1986, and the Central Bank of Nigeria (CBN) deregulated the financial sector, new banks proliferated, mainly driven by attractive arbitrage opportunities in the foreign exchange market. But relative to the pre-deregulated period, financial intermediation never took off and even declined in the 1980s and 1990s. With the bursting of the bubble during the early 1990s in a very volatile macroeconomic environment, Nigeria’s banking sector was still characterized by a high degree of fragmentation and low levels of financial intermediation up to 2004.

This paper is motivated by the CBN’s recent reforms to consolidate the banking sector by drastically increasing the minimum capital requirements from 2 billion Nigerian Naira (NN) to NN25 billion ($ US 190 million). This led to a remarkable reduction of the number of banks from 89 to 25, mainly by mergers and acquisitions, by the beginning of 2006 in a very short period of time.

We use unique bank-by-bank balance sheet and income statement information from the CBN to investigate the intermediation efficiency in the pre-consolidated banking sector during 2000-2005. We aim to analyze whether the CBN’s policy of banking consolidation can be justified and rationalized by looking at the determinants of spreads.

Interest rate spreads are often used as proxies for the efficiency of financial intermediation. If there were no market frictions and transaction costs, lending and deposit rates would be equal. But since screening and monitoring borrowers is costly, there is a wedge between the lending and deposit rates.

Our findings provide some evidence that indeed it was the right decision of the CBN to radically change the market structure of the banking sector. A spread decomposition shows that spreads have been coming down in the past years, together with an overall decline in lending and deposit rates. Larger banks have especially enjoyed lower overhead costs that have materialized in higher profit margins and also charge lower spreads than smaller banks.

2
Table 3: Trade Effects of CEFTA <u>(</u> _σ_ = 7)

|Country|Manuf<br>|acturing|Agric<br>|ulture|Min<br>|ing|Services|
|---|---|---|---|---|---|---|---|
||Tarif|Border|Tarif|Border|Tarif|Border|Border|
|||PANE|L A: CE|FTA cou|ntries|||
|ALB|25.17|66.94|53.13|288.90|0.81|12.90|128.95|
|BIH|35.23|53.77|44.37|235.07|13.59|46.12|-4.40|
|MDA|15.51|43.44|25.64|67.95|1.33|90.21|-0.01|
|MKD|29.16|54.04|56.77|125.43|22.82|95.11|283.32|
|MNE|35.14|57.92|71.12|88.38|2.01|4.01|87.11|
|SRB|31.85|45.27|46.93|96.67|4.67|45.71|116.04|
|||PANEL|B: EU co|untries a|nd ROW|||
|AUT|0.22|0.39|1.17|3.46|-0.05|-0.22|6.80|
|BEL|0.02|0.04|0.17|0.45|0.00|0.01|0.06|
|BGR|0.92|1.70|1.90|5.27|0.44|8.50|0.30|
|CYP|0.12|0.21|0.37|1.01|0.00|-0.01|0.49|
|CZE|0.13|0.24|0.37|1.04|0.10|1.62|0.10|
|DEU|0.09|0.16|0.27|0.77|-0.01|0.01|0.05|
|DNK|0.04|0.07|0.04|0.16|0.01|0.04|0.01|
|ESP|0.03|0.05|0.13|0.34|0.00|0.07|0.01|
|EST|0.02|0.05|0.02|0.09|0.00|-0.01|0.02|
|FIN|0.02|0.03|0.06|0.24|0.00|0.00|0.00|
|FRA|0.03|0.05|0.12|0.34|0.01|0.31|0.03|
|GBR|0.04|0.07|0.08|0.25|0.00|0.00|0.06|
|GRC|0.96|1.95|2.53|7.03|0.10|2.49|0.86|
|HRV|4.35|7.72|19.20|62.27|1.29|13.40|0.66|
|HUN|0.32|0.55|1.53|4.13|0.42|9.91|0.15|
|IRL|0.01|0.02|0.01|0.05|0.00|0.00|0.02|
|ITA|0.24|0.45|0.43|1.36|-0.02|0.44|0.17|
|LTU|0.04|0.09|0.13|0.43|0.00|0.05|0.11|
|LUX|0.05|0.09|0.00|0.01|-0.03|-0.12|0.00|
|LVA|0.04|0.08|0.10|0.33|0.00|0.00|0.10|
|MLT|0.17|0.41|0.03|0.14|1.17|17.01|0.10|
|NLD|0.03|0.05|0.10|0.27|0.00|-0.01|0.06|
|POL|0.13|0.24|0.41|1.23|0.11|1.41|0.02|
|PRT|0.02|0.03|0.04|0.14|0.00|-0.05|0.00|
|ROU|0.59|1.19|1.33|3.96|1.39|28.39|-0.06|
|SVK|0.21|0.36|0.67|1.83|-0.03|-0.05|0.48|
|SVN|1.54|2.70|7.47|21.51|-0.04|1.14|3.11|
|SWE|0.03|0.06|0.17|0.52|0.00|0.01|0.05|
|ROW|0.00|-0.01|0.00|-0.03|0.00|0.00|-0.01|

**Notes** : This table reports results for our CEFTA border and tariff scenario assuming an elasticity of substitution of 7 ( _σ_ = 7). Column (1) gives the country abbreviations, columns (2), (4), and (6) report the changes in total exports from our tariff scenario for manufacturing, agriculture, and mining, respectively. Columns (3), (5), (7), and (8) report the changes in total exports from our border scenario for manufacturing, agriculture, mining, and services, respectively.

23
I POLICY RESEARCH WORKING PAPER 2926

# Abstract

The objective of this paper **is** to empirically test across alternative, apparently observationally equivalent theories of currency crises. Theories of crises are often difficult to distinguish from each other based on the behavior of commonly used predictors. Using a comprehensive data set on gross external assets and liabilities for 167 countries created by the World Bank's Latin America and the Caribbean Region and the Development Research Group, this study is able to make a significant move toward redressing this shortcoming. It focuses on identifying potential crisis predictors, as well as testing the validity of the distinct transmission mechanisms implied by various theories of currency crisis. Evidence is presented in support of insurancebased models, suggesting that proxies for contingent liability accumulation are effective crisis predictors.

This paper-a product of the Office of the Chief Economist, Latin America and the Caribbean Region-is part of a larger effort **in** the region to promote a deeper understanding of the causes and consequences of balance-of-payments crises. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Holt, room 18-168, telephone 202-473-7707, email address pholt@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rshankar@worldbank.org. November 2002. (38 pages) _The Policy Research WYlorking Paper Series disseniinates the findings of work in progress to encourage the exchange of Ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countoes they represent._

## Produced by the Research Advisory Staff
44 The household sector as represented in the model is biased towards a universalistic transfer scheme since there is no distinction between the poor and non-poor in each of the seven groups, so the targeting programs are aimed at socio-economic groups that are easily identifiable.

The calculation of the transfer size can be derived from the Foster et al. (1984) poverty index: or in terms of the income frequency density function,f(y), such that where n is total population, q is the number of poor, Z is some pre-determined poverty line, _yi_ is the income of the poor household i and a is a parameter that measures the depth of poverty.<sup>3</sup> " In the case of perfect targeting, the amount needed to eliminate poverty is the sum of the difference between the income of the poor and the poverty line Z or

> **38** The head-count ratio is obtained when a = _o; PO = qln._
# **Table 8 Numbers of Safeguard Initiations and Other Safeguard Actions Notified to the WTO by Selected Latin American Countries; 1996–October 18, 2004** (blank spaces imply an entry of 0, the 0’s wee left out to reduce clutter)

||1996|1997|1998|1999|2000|2001|2002|2003|Jan. 1 -–<br>Oct. 18,<br>2004|1995–<br>2000<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|_Argentina_|||||||||||
|Initiations||1|1||1|1|||1|3|
|Preliminary measures||||||1|||||
|Definitive measures applied||1|||1|1||||2|
|Negative decision on definitive<br>measures|||||||||||
|Presumed negative|||1|||||||1|
|Ongoing|||||||||1||
|_Brazil_|||||||||||
|Initiations|1|||||1|1|||1|
|Preliminary measures|1||||||1|||1|
|Definitive measures applied|1|||||1|1|||1|
|Negative decision on definitive<br>measures|||||||||||
|Presumed negative|||||||||||
|Ongoing|||||||||||
|_Chile_|||||||||||
|Initiations||||1|4|2|3|||5|
|Preliminary measures||||1|2|||||3|
|Definitive measures applied||||1|1|1|1|||2|
|Negative decision on definitive<br>measures|||||2|1|2|||2|
|Presumed negative|||||1|||||1|
|Ongoing|||||||||||
|_Colombia_|||||||||||
|Initiations||||1|||||1|1|
|Preliminary measures|||||||||||

52
17 3. We may define a partial order with respect to development; a country _j dominates_ a country _k_ if With regard to this partial order, no country in the sample dominates all others. Thus, we can say there exists no most developed European country. Conversely, however, there are five countries that are undominated by any other: Denmark (DK) , Iceland (IS), Germany (DE), the UK, and the Netherlands (NL). These data are from 2005, and doubtless Iceland, post-crash, no longer enjoys this status.

Table 1 presents the same data as figure 1.

As noted, Ferreira and Gignoux (2011) calculate a similar statistic to for six Latin American countries. Their calculation differs from the one presented here using the SILC data in two ways: they have a different set of circumstances, and they use a different measure of inequality. I have calculated an index for Brazil, using a data set which reports income of workers for a typology whose circumstances are race, gender of the head of household in which the worker was raised, and urban-rural<sup>14</sup> . There are four races: white, mixed, black, and ‘other’ – thus 16 types. I limited my analysis to nine types, which comprise 94.5% of the sample, not including the four types of ‘other’ race, or the three rural types with female head-of-household parent. For this population, we compute , which is surprisingly high – only 1.6% of income inequality is attributed to these circumstances. This contrasts with the IOR computation of Ferreira and Gignoux (2011), in which, in Brazil, about 32% of inequality is due to 14 I thank Sean Higgens for providing me with the Brazilian data, which were collected as part of the Commitment to Equity project. See Higgens and Pereira (in press) for details.
more of the initial population, regardless of whether these were battle deaths or other casualties. Note that most war events in the data took place over a large number of years—for example, 1981 to 1989 for Nicaragua, or 1983 to the present for Sudan—so the number of deaths in any given conflict year is substantially smaller. The second definition of conflict is based on the UCDP/PRIO Armed Conflict Dataset (Gleditsch et al. 2002). These data identify “internal armed conflicts”, “interstate armed conflicts”, or “internal internationalized armed conflicts” for every country and year in our data, and also provide measures of the intensity of the conflict. We take the most conservative approach and create an indicator variable for any conflict. In both cases, we then parallel the analysis for rainfall periods, interacting the indicator for conflict with log per capita GDP, and including this interaction and the main effects for log per capita GDP and conflict in the regressions. The results, presented in the third panel of Table 3, show that controlling for possible conflict impact has no appreciable effect on our estimates of the association between infant mortality and log per capita GDP.

Other factors that have been stressed in the literature that could be correlated with both GDP and infant mortality shocks, such as education and the quality of institutions (Deaton 2006), are also unlikely to account for the basic pattern of results we observe. The results in Table 2 show that controlling for mother education has little effect on our estimates. Moreover, our approach focuses on short-term (yearly) changes in per capita GDP and infant mortality, and one would expect factors such as education and the quality of institutions to follow trends or cycles that are considerably longer than single-year changes. Nevertheless, we make various attempts to correct for possible shocks to institutional quality that could be correlated with both the GDP and infant mortality trend deviations that form the basis of our estimates.

16
- 23 -

# 4.4 Wage Rates

# 4.4.1 Real Consumption Wages

In Table 4 we present some available information on rates of growth of real consumption wages in the formal sector for three periods: the mid1960's to one year after the 1973 oil shock; the remainder of the 1970's and the early 1980's. Whenever possible, we have used the wage series for construction workers on the grounds that this is least affected by improved skill levels over time. The data for 1974/80 and 1980/84 in the six Latin American countries refer, however, to average manufacturing sector wages and are likely, therefore, to overstate the wage growth enjoyed by a worker of a given skill level. This effect is neverthelesss likely to be small. The urban consumer price index is used as the deflator in each country.

There are two main facts that stand out: (i) there is little prima facia evidence on downward real wage rigidity as often suggested by opponents of exchange rate devaluation. Nine out of the sixteen countries for which data have been presented have experienced a fall in real wages over one or more periods. A recent survey of real wage growth over a much wider range of LDC's, ILO (1987) has further suggested that in fact, since the early 1970's, real wages have fallen in most LDC's. The same evidence is pointed out by Riveros (1988d) with regard to real labor costs -- considering both wage and non-wage costs of labor -- in 21 LDCs (Table 7). Whether this observed trend is due to the pressure of market forces, or to institutional intervention, or to some combination of both remains a question to be discussed later in this paper. The broad picture across countries, is however, that real wages have tended to fall in sub-Saharan Africa and in some Latin American countries, but less so in South and East Asia. (ii) By and large, real wage growth has roughly followed that of GDP per capita -- a pattern consistent with the expected outcome of market forces. This does conflict with the predictions of
# **1 Introduction**

Global warming is expected to increase temperature and rainfall averages and their variability (IPCC, 2007).<sup>2</sup> Such changes are particularly worrying for developing countries because large portions of their populations rely on climate dependent activities, and have limited income to adapt.

In response, economic analysis has attempted to estimate the possible impacts of climate change in developing societies. One strand of literature focuses on the link between income and climatic factors often looking at cross-country data, with most studies finding a negative relationship between income and temperature (Dell et al., 2009; Bansal, 2009).

Another strand focuses on the effects of climate-related disasters on macroeconomic indicators or country-level variables, including GDP or its growth. Studies differ in their techniques, data and findings: Some encounter negative effects (Auffret, 2003), while others encounter positive effects (Skidmore and Toya, 2002). Loayza et al (2009) reconcile the seemingly contradictory results estimating the medium-term effects of climate-related disasters such as droughts, floods, storms (separately and simultaneously) on economic growth using a model with three main sectors (agriculture, industry, and services) and with the whole economy. Severe disasters have adverse effects regardless of type, but moderate floods, for instance, do increase industrial growth.

All these studies of climate change effects or climatic disasters focus on local and economy-wide effects on output. However, it is known that output measures are an imperfect way to gauge society’s overall well-being. Therefore, this paper will focus on the effects of climate change on poverty, as opposed to economic growth.

A few studies have analyzed the possible impact of climate change on poverty in Mexico, some modeling the channels and the heterogeneity of impacts across income groups while others concentrate on poverty at the municipal level. Hertel et al. (2010)<sup>3</sup> use a global Computable General Equilibrium model (the Global Trade Analysis Project or GTAP) to mimic the effect of productivity shocks in agriculture (model the consumption and production for all commodities of the national economy), and then link those estimates to household data. They use three

> 2Climate change is defined as the slow change in average temperatures and average precipitation predicted to result from the build-up of greenhouse gases in the atmosphere and not of climate variability.

> 3Hertel, T., M. Burke and D. Lobell. (2010) “The Poverty Implications of Climate-Induced Crop Yield Changes by 2030” GTAP Working Paper No. 59.

2
# **Figure 7**

<!-- Start of picture text --> Average expected purity of powder cocaine in the US<br>(Information from purchases only)<br>0,9<br>0,8<br>0,7<br>0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0<br>< 2 grams 2 to 10 grams 10 to 50 grams > 50 grams<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003*<br><!-- End of picture text -->

* Based on information from only the first two quarter of this year. Source: ONDCP (2004), based on STRIDE.

# **Figure 8**

<!-- Start of picture text --> Average purity of powder cocaine in the US<br>(Information from purchases, seizures, and other enforcement activities)<br>1<br>0,9<br>0,8<br>0,7<br>0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0<br>< = 10 grams 10 to 50 grams<br>50 to 750 grams > 750 grams<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003*<br><!-- End of picture text -->

- Based on information from only the first two quarter of this year. Source: ONDCP (2004), based on STRIDE.

55
ISO (2002). The ISO survey of ISO 9000 and ISO 14000 certificates, Eleventh cycle – 2001. Geneva: ISO Central Secretariat.

La Porta, R. and A. Shleifer (2014). Informality and Development. _Journal of Economic Perspectives_ 28 (3): 109-126.

La Porta, R., and A. Shleifer **(** 2008). The Unofficial Economy and Economic Development. _Brookings Papers on Economic Activity_ Fall: 275 – 352.

Loayza, N. V. (2016). Informality In the Process of Development and Growth. _World Economy 39_ (12): 1856–1916.

Maloney, W. F. (2004). Informality Revisited. _World Development_ 32 (7): 1159-1178.

McCann, B. T. and M. Bahl (2017). The Influence of Competition from Informal Firms on New Product Development. _Strategic Management Journal_ 38(7): 1518-1535.

Medase, S.K. and S. A. Basit (2023). Trademark and Product Innovation: The Interactive Role of Quality Certification and Firm-level Attributes. _Innovation and Development_ , 13(1): pp.1-41.

Meemken, E.M., P.C. Veettil, and M. Qaim (2017). Toward Improving the Design of Sustainability Standards-A Gendered Analysis of Farmers’ Preferences. _World Development_ 99: 285–298. https://doi.org/10.1016/j.worlddev.2017.05.021.

Mendi, P., and R. Costamagna (2017). Managing Innovation Under Competitive Pressure from Informal Producers. _Technological Forecasting and Social Change_ 114(January): 192 – 202.

Minard, P. (2016). Signalling Through the Noise: Private Certification, Information Asymmetry and Chinese SMEs’ Access to Finance. _Journal of Asian Public Policy_ 9(3): 243-256.

Montiel, I. and B.W. Husted (2009). The Adoption of Voluntary Environmental Management Programs in Mexico: First Movers as Institutional Entrepreneurs. _Journal of Business Ethics_ 88: 349-363.

Montiel, I., B. Husted, and P. Christmann (2012). Using Private Management Standard Certification to Reduce Information Asymmetries in Corrupt Environments. _Strategic Management Journal_ , 33: 1103–1113.

Moser, C.O. (1978). Informal Sector or Petty Commodity Production: Dualism or Dependence in Urban Development? _World development 6_ (9-10): 1041-1064.

Naveh, E. and A. Marcus (2005). Achieving Competitive Advantage Through Implementing a Replicable Management Standard: Installing and Using ISO 9000. _Journal of Operations Management_ 24(1): 1-26.

34
19 on ex-post facto monitoring.<sup>54</sup> These units and SSBs face similar challenges, relating, in particular, to independence, and competence.

Beyond internal arrangements, the broader _Shariah_ governance framework may include features put in place by regulators, as well as the provision of financial information services to persons outside the institution. Among regulatory arrangements, centralized SSBs are the most noteworthy in relation to _Shariah_ governance. While there are significant differences across countries, centralized SSBs are usually concerned with ex-ante monitoring, mostly understood as standardization of _Shariah_ interpretation, and ex-post monitoring of _Shariah_ compliance. They are also concerned with issues relating to upholding _Shariah_ compliance, and offer arbitration and recourse to settle _Shariah_ disputes between members of the same SSB.<sup>55</sup>

||Table III - External_Shariah_CG<br>|institutions by country*<br>|
|---|---|---|
|Country|Centralized SSB or High_Shariah_<br>Authority or Fatwa Board|Islamic Rating Agency|
|**_Jordan_**|No|No|
|**_Malaysia_**|�|�|
|**_Sudan_**|�|No|
|**_Bahrain_**|No†|No†|
|**_Kuwait_**|�|No|
|**_Pakistan_**|�|No|
|**_UAE_**|�|No|
|**_Indonesia_**|�|No|
|* Annex IV details<br>† Bahrain is the se<br>an international sc|the names and powers of these departments/authorities.<br>at of the IIFM and the IIRA that respectively set standa<br>ale.|<br>rds for Islamic jurisprudence and rate Islamic instruments on|
|Source: Official co|untrywebsites and central bank Annual Reports.||

> 54 This is the case of large IIFS where the SSBs may not be able to assess large volumes of transactions. Therefore, separate _Shariah_ control departments have been established. This seems to be the case in al Rajhi Banking and Investment Corporation and Dubai Islamic Bank.

> 55 Annex IV presents names and powers of these organizations. It focuses in particular of the powers of central SSBs.