rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6195

# **Abstract**

This paper examines the impact of bank ownership on credit growth in developing countries before and during the 2008-2009 crisis. Using bank-level data for countries in Eastern Europe and Latin America, it analyzes the growth of banks’ total gross loans as well as the growth of corporate, consumer, and residential mortgage loans. Although domestic private banks in Eastern Europe and Latin America contracted their loan growth rates during the crisis, there are differences in foreign and government-owned bank credit growth across regions. In Eastern Europe, foreign bank total lending fell by more than domestic private bank credit. These results are primarily driven by reductions in corporate loans. Furthermore, government-owned banks in Eastern Europe did not act counter-cyclically. The opposite was true in Latin America, where the growth of governmentowned banks’ corporate and consumer loans during the crisis exceeded that of domestic and foreign banks. Contrary to the case of foreign banks in Eastern Europe, those in Latin America did not fuel loan growth prior to the crisis and did not contract lending at a faster pace than domestic banks during the crisis.

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 mmartinezperia@worldbank.org and rcull@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
||**3%**|**6%**|**8%**|**10%**|**12%**|**14%**|
|---|---|---|---|---|---|---|
|Burkina Faso|0.43|0.66|0.82|**0.99**|1.17|1.35|
|Burundi|0.57|0.72|0.83|**0.95**|1.08|1.20|
|Cameroon|0.26|0.42|0.54|**0.67**|0.80|0.93|
|Cape Verde|0.85|1.16|1.39|**1.63**|1.87|2.12|
|Central African Republic|0.12|0.23|0.31|**0.39**|0.48|0.56|
|Comoros|3.47|3.73|3.93|**4.13**|4.34|4.55|
|Congo, Rep.|0.11|0.29|0.42|**0.56**|0.70|0.84|
|Côte d’Ivoire|1.34|1.57|1.74|**1.92**|2.10|2.28|
|Ethiopia|0.58|1.02|1.35|**1.69**|2.04|2.40|
|Gabon|0.03|0.16|0.26|**0.36**|0.47|0.58|
|Gambia, The|4.50|5.04|5.42|**5.83**|6.25|6.68|
|Ghana|0.12|0.26|0.35|**0.45**|0.56|0.66|
|Guinea|1.50|1.74|1.91|**2.10**|2.29|2.48|
|Kenya|0.20|0.43|0.60|**0.78**|0.97|1.16|
|Lesotho|-0.69|-0.22|0.13|**0.49**|0.86|1.23|
|Liberia|0.24|0.29|0.32|**0.36**|0.39|0.43|
|Madagascar|1.69|1.90|2.05|**2.21**|2.38|2.54|
|Malawi|0.77|1.45|1.95|**2.48**|3.02|3.58|
|Mali|0.94|1.08|1.18|**1.29**|1.40|1.51|
|Mauritania|1.12|1.28|1.40|**1.52**|1.65|1.78|
|Mauritius|-0.23|0.04|0.23|**0.43**|0.64|0.85|
|Mozambique|0.17|0.47|0.69|**0.92**|1.16|1.39|
|Niger|0.26|0.35|0.41|**0.48**|0.54|0.61|
|Nigeria|0.36|0.43|0.48|**0.54**|0.59|0.65|
|Rwanda|0.74|0.86|0.95|**1.04**|1.13|1.23|
|São Tomé and Príncipe|5.15|5.52|5.80|**6.08**|6.37|6.66|
|Senegal|1.69|1.89|2.03|**2.18**|2.33|2.48|
|Seychelles|-0.94|-0.67|-0.47|**-0.26**|-0.05|0.18|
|Sierra Leone|0.49|0.64|0.75|**0.87**|0.99|1.11|
|South Africa|1.36|2.17|2.78|**3.43**|4.10|4.78|
|Sudan|0.68|0.97|1.19|**1.41**|1.64|1.88|
|Swaziland|0.38|0.73|0.98|**1.24**|1.51|1.78|
|Tanzania|-0.02|0.10|0.19|**0.28**|0.38|0.47|
|Togo|1.13|1.32|1.46|**1.60**|1.75|1.90|
|Uganda|-0.23|-0.17|-0.12|**-0.07**|-0.03|0.02|
|Zambia|0.49|0.79|1.00|**1.23**|1.45|1.69|
|Zimbabwe|2.08|3.32|4.23|**5.19**|6.17|7.17|
|Maximum|5.15|5.52|5.80|**6.08**|6.37|7.17|
|Minimum|-0.94|-0.67|-0.47|**-0.26**|-0.05|0.02|
|Median|0.49|0.73|0.95|**1.04**|1.17|1.39|

_Source_ : World Bank staff calculations The discount rate in this study represents the cost of capital. Aside from limited concessional financing provided by international organizations, most utilities in SSA are not able to obtain loans with long tenor. A discount rate of 6 percent is tested to represent the lowest estimated cost of capital in real terms the highest-performing utilities could access. Reducing the discount rate to 6 percent is equivalent to reducing capex by about 35 percent (depending on the number of years over which capex is amortized) at the base discount rate of 10 percent, and conversely increasing the rate to 14 percent is equivalent to increasing capex by about 40 percent. A lower bound of 3 percent is intended to represent concessional financing as well as net-of-inflation government bond rates in some countries, such as South Africa. An upper bound real discount rate of 14 percent is tested to represent highest-risk contexts.

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Ultimately, the short- to medium-term poverty effects of higher international food prices will be determined by: (1) the degree of pass-through; (2) the incidence and severity of poverty among net food producers versus net food consumers; and (3) the extent to which higher food prices translate into higher income for farmers (in the form of profits and wages). The degree of pass-through will be, in turn, determined by domestic market conditions such as: government intervention in the form of subsidies or price controls, infrastructure and market access, the degree of domestic competition and trade barriers among others. Net food production/consumption patterns are determined by the importance of the agricultural sector as an income source of the poor and the proportion of total household budget allocated to food consumption. Finally, the relationship between higher food prices and farmer incomes is a function of the heterogeneity in domestic price transmission among large versus small farmers, and the ability of rural factor (labor) markets to adjust to changes in prices of final products.

# 3 International vs. Domestic Food Prices

Between January 2005 and December 2007, the international food price index increased 74 percent.<sup>4</sup> Is this a good indicator of the reduction in purchasing power suffered by consumers in developing countries? The international food CPI reflects changes in the international food prices weighted by commodity-specific global trade volumes. In a world where as little as 7 percent of total food consumption is being traded internationally, the international and domestic food CPIs are only marginally related. Consumption patterns can be quite different between countries with the importance of internationally traded commodities in domestic food CPIs varying across countries. The relevant price changes for welfare analysis are the domestic food CPIs which, although they have shown a rapid increase between 2005 and 2008, have a growth rate that is far from being as large as the increase shown by the international food CPI.

**Figure 2: Distribution of Cumulative Increases in Nominal Food Prices (LCU, Jan 2005 – Dec 2007)**

<!-- Start of picture text --> Cumulative Increase in<br>International Food Prices = 74 %<br>0 20 40 60 80 100<br>Percentage Change in Price<br>20<br>15<br>10<br>5<br>Percentage of Developing Countries<br>0<br><!-- End of picture text -->

> 4 Using figures from The World Bank (DECPG).

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surrounding rural areas and towns and offers employer-provided transport to work, life and health insurance, and a subsidized cafeteria (Sutton and Kellow 2010).

## **Box 1: Off-farm Opportunities for Agricultural Employment**

The labor market effects of high-valued agriculture are evidenced in a number of SSA countries. In 1985, export of high-value agricultural products in SSA countries accounted for just 14 percent of their agricultural and food exports; by 2005, the share had risen to 30 percent, with many jobs created along the way. In Madagascar, the export of vegetables has fully relied on about 10,000 smallholders contracted for procurement of primary produce. In other instances, production of primary produce has been vertically integrated with large estate farms, as is the case with tomato and bean exports in Senegal; the jobs created are for wage-earners in processing units and pack houses. The table below gives several examples of employment created in export horticulture chains. Horticulture is generally labor intensive, providing strong poverty alleviation benefits, especially for women.

||**Employment in S**<br>|**SA Export Hort**<br>|**iculture Supply Chains**<br>||
|---|---|---|---|---|
|**Country**|**Commodity**|**Year of survey**|**No. of employees in the**<br>**FFV agro-industry**|**Share of female**<br>**employees**|
|Cameroon|Banana|2003|10,000|--|
|Cote d’Ivoire|Banana<br>and<br>pineapple|2002|35,000|--|
|Kenya|Flowers<br>Fruits and vegetables|2002<br>2002|40,000-70,000<br>2,000,000|75%|
|Senegal|French beans|2005|12,000|90%|
||Cherry tomatoes|2006|3,000|60%|
|Uganda|Flowers|1998|3,300|75%|
|Zambia|Vegetables<br>Flowers|2002/03<br>2002/03|7,500<br>2,500|65%<br>35%|
|South Africa|Deciduous fruit|1994|283,000|53%|

_Source_ : Maertens, Minten and Swinnen 2009.

Food processing for local markets is another growth area for rural employment. A proliferation of new towns through _in situ_ urbanization is generating increased demand for processed foods, with corresponding increased investment in processing. Increased attention to food safety at the level of public policy will help to improve conditions within the processing plants, since the conditions that assure safe products also contribute to improved hygiene and safety for workers. The regulatory bar for wages and working conditions should, however, not be set so high as to choke off investment, since this would eliminate jobs and depress demand for primary production. The surge in food imports in Africa since 2003 reflects the underdevelopment of local food processing. Remedying this underdevelopment will create jobs and raise returns to investments in primary agriculture.

# **7. Constraints to Enhanced Agricultural Employment**

The farms that many young Africans know from childhood are small and worked with a low level of mechanization. Holdings of one to two hectares predominate, with the hand hoe and machete as the most common implements (Nagayets 2005). World Bank data from four countries show that land use, both owned and rented, increases with age, and that the average plot size even for older farmers often remains well under one hectare (Table 4).

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positively correlated with inflows to East and Southeast Asia and insignificantly correlated with FDI to Latin America. In contrast, Marcereau (2005) finds a substitution effect between China and other Asian countries, although this effect is driven by two countries, Singapore and Myanmar. Garcia-Herrero and Santabarbara (2005) use bilateral data on FDI among source-host country pairs during 1984-2001 and do not find a substitution effect between Chinese and Latin American FDI. However, they do find a negative relationship between China and Latin American inflows when they focus on 1995-2001, especially for Mexico and Colombia. Eichengreen and Tong (2005) use a “gravity model” of bilateral FDI that controls for both countries’ GDPs, GDP per capita, and other variables used in the standard trade gravity model, such as distance and common language. They then augment this model with China’s FDI inflows, and find that China has a positive effect on inflows to others developing countries but no effect on Latin America.

There is a gap in the literature when it comes to estimating the impact of India on foreign investment in other economies. This is one of the contributions of this paper.

We follow Eichengreen and Tong (E.T.) by including FCS in China and India in FCS regressions using bilateral FCS data collected by OECD and UNCTAD. One potential pitfall of ET, however, is their use of the gravity model, which is not grounded in FDI theory. We draw on an alternative specification, namely the “Knowledge-Capital Model” (KCM), which is grounded on the theory of the multinational enterprise and has been estimated in recent papers by Carr, Maskus and Markusen (2001), Blonigen, Head and Davis (2003), and Blonigen and Davies (2002). We augment the KCM with Chinese and Indian FCS and interactions of these variables with a dummy for LAC countries. A 4
<!-- Start of picture text --> Services Density India and China<br>X<br><<br>Y<br>7<br>2 Ne<br>3)<br>io3°._<br>cil<br>cS<br><x<br>Xv<br>oP<br>0 2. 4 6 8<br>Initial Service Employment Density (logs)<br>——— India 1999-2004 ———— China 2000-200<br>Source: NSS and China City Statistical Yearbooks<br><!-- End of picture text --> <!-- Start of picture text --> é a Services: NSS Poe. Services: NSS<br>. | Predicted Growth Rate “| Counterfactual Growth Rate<br>- <001 [0.031-0.090 6 | Fl<0055 [i 0.059-0.062<br>5 [)0.011-0.030 0.09 [[)Ne Data = [)0.056-0.058 J >0.062 [Ne Data<br>; bag oi ¢ ty iw) / Y &<br>_A ee ae Goce a5 g, ~ » ? a7<br>cfe obwBen ~ S e e es *g. h fi ia %“2 3 ‘ieg ,<br>> Ade end Pr & Pde<br>x phy part at ase pe ya par!<br>> ‘ Nt \ he Se ‘<br>rr ‘ & ‘<br>wa. 7 ‘ 7<br>a ‘ oN ‘ a y ‘<br><!-- End of picture text -->
achieved with taxes.<sup>12</sup> This fits well with basic theory. It establishes that unless one accepts and incorporates effects such as myopia and excessive discounting, fuel efficiency standards will be a costly way of reducing consumption relative to fuel taxes. This is illustrated well by Fischer et al.(2007) who develop a general model to analyze increases in fuel economy standards, allowing for alternative assumptions about how consumers value fuel efficiency. Those assumptions allow tighter efficiency standards to bring anything from moderate welfare gains to substantial welfare losses, based on damage costs estimates from the USA (i.e. a cost benefit analysis, rather than comparison with tax).

Gerard and Lave (2003), in contrast, argue that there is a huge gap between social and private willingness to pay for oil conservation. The source of this mismatch is not due to lack of information or traditional sources of market failure as they argue that “...we argue that CAFE is justified even for rational, well-informed consumers, since there are social benefits of fuel conservation that private decision makers do not internalize.” One argument in favor of a mandated increase in fuel efficiency concerns a particular aspect of household decisions, perhaps to be regarded as market failure. These arguments (see National Academy of Sciences (1991), and Haussmann (1979)), are based on empirical studies indicating that low-income households use higher discount rates when they value future fuel savings. Accordingly, consumers discount the benefit of energy conservation at a higher rate than what a society would desire, and fuel economy standards can address this.<sup>13</sup> Another argument for mandated fuel standards in relation to gasoline taxes concerns equity and public finance. According to this argument, uniform gasoline taxes are more painful to

> 12 See also Charles River Associates (1991) for details of the calculation of costs.

> 13 The empirical estimate from Espey and Nair (2005) suggests that there are cases with no evidence of excessive discounting. Generally, the research testing the hypothesis of market failure through excessive discounting is not rich, neither providing clear results on the empirical phenomenon nor in terms of policy implications.

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Figure 9: Past intergenerational mobility and subjective ranking <!-- Start of picture text --> Probability of positioning in given steps of the economic ladder<br>Steps 1 and 2 Steps 5 and 6 Steps 9 and 10<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile of Consumption Decile of Consumption Decile of Consumption<br>I have done worse than my paren ts I have done the same as my parents<br>I have done better than my parents<br>.6 .6 .6<br>.5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>Probability of positioning Probability of positioning Probability of positioning<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> Note: this graph plots the probability of respondents positioning themselves in different steps of the economic ladder by objective decile of consumption and perceptions on whether the respondent has done better than his/her parents . The left panel plots the probability of choosing steps 1 and 2; the center panel plots the probability of choosing steps 5 and 6 and the right panel plots the probability of choosing steps 9 and 10.

propose to look at whether subjective ranking shows the same pattern as other well-being measures in their relationship with relative income using LiTS data.

In section 3.1 we have already discussed the role of ranking within reference groups as a source of information for individuals’ perception about their position in the income distribution. In the analysis that we carry out in the following lines we are using a different working assumption – that individuals’ response about their position in the income ladder should be understood as a measure of well-being. It is, thus, a different analysis, for which the empirical specifications are different. In order to study the role of relative income on self-rated economic welfare, we can assume the following relation for each individual

# _Perceived Position_ = _SUBJ_ ( _c,Rel,X_ )

with _Perceived Position_ the response on the economic ladder, _c_ stands for the per-capita consumption and _Rel_ for the relative position of an individual in his or her reference group. The vector of variables X includes the same variables as in the previous section. In terms of reference groups, we choose to look at the same we referred to in section 3.1: the region-education group, where education is defined as having or not a university degree, and the age-education group, where age groups are three (belo 35 years of age, between 35 and 55, and above 55). The rank of each individual is given by the rank of his consumption level among the consumption level of other members of the chosen reference group. We normalize ranks from 0 to 1, being 0 the lowest consumption level in 24
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# D. Domestic Factors

16. On the domestic front, necessary but not sufficient conditions for long-term growth are adet,uate investment levels and a favorable policy framework to ensure a stable incentive climate. The two are closely interrelated.

17. Savings-Investment. A recent survey of World Bank economists shows the average GDI/GDP ratio for 90 developing countries in 1988 was 25.5 percent. This is close to that of the previous year (Table 5), and is consistent with historic levels in countries that experienced growth rates around 4 percent. Higher performers typically had shares closer to 30 percent or more. Sub-Saharan Africa, at less than 15 percent, similar to its average of 15.7 percent in the eighties, has too low an investment rate to offer any reasonable prospects for per capita income growth (at the aggregate level). This share compares to 21.4 percent in the 1973-80 period. In Latin America the share of investment in 1987 was 16 percent, and it is expected to be slightly lower for 1988. This continues a disturbing downward trend from 23.8 percent in the seventies to an average of 18.8 percent in the period 1980-87. Even though policy reforms should lead to efficiency gains, it seems unrealistically optimistic to expect annual growth rates of real output around 4 percent, for any sustained period, if investment ratio remains low in these countries. These levels contrast with average levels of 23 percent in EMENIA and Asia over the period 1980-87.
||**Number of**<br>**persons**<br>**(millions)**|**Percent of**<br>**population**<br>**(over 15)**|**Trends**|
|---|---|---|---|
|**Asia**|7.46|0.29||
|- India|2.68|0.40|Slower growth|
|- China|0.96|0.10|Increasing|
|- Pakistan|0.74|0.90|Stable to declining|
|- Thailand|0.27|0.60|Continued decline|
|- Other|2.81|0.34|Mixed by region|
|**Europe**|4.56|0.70||
|- Western Europe|1.57|0.42|Stable to declining|
|- Russian Federation|2.39|2.00|Strongly increasing|
|- Other Eastern Europe|0.60|0.38|Mixed|
|**Oceania**|0.14|0.63|Declining|
|**Americas**|1.86|0.30||
|- United States|1.34|0.60|Declining|
|- Other|0.52|0.13|Some increases|
|**Africa**|0.92|0.20|Increasing|
|**Global**|14.94|0.35|Increasing|

Source: UNODC (2003, pp. 107-128, 336); U.N. World Population Prospects 2002 Revision

# **Table 1: Annual Prevalence Estimates of Opiate Abuse, 2000-2001**

The data presented so far report only numbers of users. What are needed to understand the market are estimates of quantities. Almost no data are available on the average quantities consumed annually by addicts in each country.<sup>9</sup> This reflects the fact that users can report only how much they spend on cocaine and heroin, not how much of the active drug they purchased, since the purity is highly variable and cannot be observed. There is some evidence to suggest that U.S. heroin addicts consume less per annum than their counterparts in Europe but absent more specific data, it is reasonable to assume that for the rest of the world the distribution of quantities consumed does mirror the distribution of users.

> 9 A forthcoming study by Badillo et al. will provide the first fully documented quantity estimate outside the United States.

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