rafmacalaba/datause-encoder-data huggingface dataset:
WPS3769

# **NONPERFORMING LOANS IN SUB-SAHARAN AFRICA: CAUSAL ANALYSIS AND MACROECONOMIC IMPLICATIONS**

By

# Hippolyte Fofack<sup>1</sup>

**Abstract** : This paper investigates the leading causes of nonperforming loans during the economic and banking crises that affected a large number of countries in Sub-Saharan Africa in the 1990s. Empirical analysis shows a dramatic increase in these loans and extremely high credit risk, with significant differences between the CFA and non-CFA countries, and substantially higher financial costs for the latter sub-panel of countries. The results also highlight a strong causality between these loans and, economic growth, real exchange rate appreciation, the real interest rate, net interest margins and interbank loans, consistent with the causality and econometric analysis, which reveal the significance of macro and microeconomic factors. Indeed, the dramatic increase in these loans is largely driven by macroeconomic volatility and reflects the vulnerability of undiversified African economies, which remain heavily exposed to external shocks. Simulated results show that macroeconomic stability and economic growth are associated with a declining level of nonperforming loans; whereas adverse macroeconomic shocks coupled with higher cost of capital and lower interest margins are associated with a rising scope of nonperforming loans. These results are supported by long-term estimates of nonperforming loans derived from pseudo panel-based prediction models.

JEL Classification Numbers: C33, E44, G21 Keywords: CFA, interbank loans, moral hazard, non-CFA, nonperforming loans, impaired loans, pseudo panel-based prediction models.

## World Bank Policy Research Working Paper 3769, November 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 The author would like to thank Bankscope for making the data on banks assets structure and portfolio available. This database was complemented by additional information on banks ownership structure and institutional arrangements provided by experts and officials from national and regional Central Banks. The author wishes to thank these officials for their support. The author is particularly thankful to Nihal Bayraktar, Antonin Dossou, Ibrahim Elbadawi, Barthélémy Kouézo, Raj Nallari and Jean-Claude Tchatchouang, for their valuable comments. The author also wishes to thank the anonymous referees for their comments on an earlier version of this paper.
|4.5.1|Unemployment Insurance: Impact on Living Standards<br>. . . . . . .|. . . . . .<br>63|
|---|---|---|
|4.5.2|Unemployment Insurance: Impact on Productivity . . . . . . . . . .|. . . . . .<br>67|
|4.5.3|Unemployment Insurance: Impact on Social Cohesion<br>. . . . . . . .|. . . . . .<br>67|
|4.5.4|Pension System: Impact on Living Standards . . . . . . . . . . . . .|. . . . . .<br>67|
|4.5.5|Pension System: Impact on Productivity<br>. . . . . . . . . . . . . . .|. . . . . .<br>69|
|4.5.6|Paid Family Leave: Impact on Living Standards<br>. . . . . . . . . . .|. . . . . .<br>70|
|4.5.7|Paid Family Leave: Impact on Productivity . . . . . . . . . . . . . .|. . . . . .<br>70|
|**5**<br>**Interaction**|**between PMR and LMR**|**71**|
|**6**<br>**Conclusion**||**78**|

3
8 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity where θ� and γ� are city and year fixed effects. In order to estimate this regression, it is necessary to have the actual areas of the urban extents of all the studied cities for, at least, two different years.

3. The estimated urban extent of city _i_ in year _t_ consists of the largest patch of developable land within the circle of ratio r� �,� calculated as:

# **4 Data**

# _4.1 Study region_

Our analysis focuses on Latin American and Caribbean (LAC) cities with populations over 50,000 people in 2010. While LAC is the most urbanized region on the planet, and the builtup areas continue to expand despite deceleration in population growth (UN-Habitat, 2012), regional averages hide large variability in the distribution of urban population across countries. Further, the region is characterized by large diversity of climatic conditions, including from tropical to temperate climates; the terrain also shows high variability in altitudes due to the presence of the Andean Mountain Range. That diversity in climate and topographic conditions translates into a high diversity in the shape that urban areas have taken. On the one hand, there are cities located in the mountains constrained by hilly terrain; on the other hand, there are also some cities located in the lower lands in flat areas, constrained only by the presence of large rivers or lakes, and yet other cities have grown located at the Caribbean and Pacific coasts, with shapes that reflect the coastline.

# _4.2 Urban extent delineation_

# _4.2.1 The DMSP-OLS images_
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# **Table 15-4. Infrastructure and services (cont’d)**

|||Nairo<br>|bi<br>||Dak<br>|ar<br>|
|---|---|---|---|---|---|---|
||P|oor|Nonpoor|P|oor|Nonpoor|
|Indicator|N|Value|N<br>Value|N|Value|N<br>Value|
|Garbage disposal system (% of households)_d_|1,282||473|1,613||345|
|No organized system||88.8|84.9||25.4|15.7|
|Dumping in own neighborhood||79.0|75.7||24.7|14.5|
|Burning/burying in own compound||9.8|9.2||0.7|1.2|
|Organized system||11.2|15.1||74.6|84.3|
|Private collection system||10.1|13.1||67.3|79.2|
|City/Municipal collection system||0.7|1.4||4.0|3.3|
|Others||0.4|0.6||3.3|1.8|
|Graywater disposal/drainage|||||||
|% of households with the following “graywater” disposal system: d|1,282||473|1,615||345|
|Pour into the drain||71.0|70.0||6.50|6.62|
|Pour into the road or pavement||19.4|16.8||63.03|57.90|
|Pour into pit latrine||1.0|0.7||||
|In a septic tank|||||1.86|5.55|
|Puisard|||||5.98|10.11|
|In an old well|||||1.33|2.15|
|Caniveaux|||||3.61|3.18|
|Other||8.5|12.5||17.68|14.49|
|% of households with drain outside house|1,282|56.4|473<br>60.5|1,615|6.4|345<br>8.4|
|% of households whose drain works most of the time|1,282|25.0|473<br>25.7|122|73.8|32<br>80.9|

_Note:_ Statistical significance for difference in the corresponding indicator value between “poor” and “nonpoor” households: (1) In Nairobi: “a”— 1% level; “b”—5% level; “c”—10% level; (2) In Dakar: “d”—1% level; “e”—5% level; “f”—10% level.

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# **c. Foreign language (English)**

<!-- Start of picture text --> Saber 11 (2005-I to 2007-I)<br>Scores in foreign language - English (z-scores)<br>Saber PRO (2011-II)<br>Scores in foreign language - English (z-scores)<br>.4<br>0<br>Frequency<br>-1 Teacher (other areas) Teacher (quant. reas.) Teacher (nat. lang.) Philosophy Geology Teacher (for. lang.) Political Science 1<br>.4<br>0<br>Frequency<br>-1 Teacher (other areas) Teacher (quant. reas.) Teacher (nat. lang.)Philosophy Geology 1 Political Science Teacher (for. lang.)<br><!-- End of picture text --> Source: Authors’ calculations based on ICFES data.

To assess relative learning mobility we estimate: 0 1 2 3 4 , (1) ii,1 ii ii,1 ii,0 ii,1 ii,0 ii,1 yy = ββ + ββ XX + ββ tt + ββ yy + ββ �tt ∗yy �+ εε where represents the z-score attained in Saber Pro by individual ; is a vector of observable ii,1 ii characteristics; yy is a dummy variable that takes the value 1 if the individual ii XX is found in Saber ii,1 PRO as an education major and 0 if she/he is in another program; tt is the z-score attained in ii ii,0 Saber 11 by _i_ ; is an idiosyncratic error term. yy ii,1 εε The vector of observable characteristics includes: gender; parents' education; family size; semester in which the student took Saber 11 and dummies by administrative unit where the student took Saber 11. We do not include observable school characteristics as they might act as confounding factors –we do not know if the student changed schools previous to his/her senior year of high school. Furthermore, university characteristics might act themselves as bad controls. They might be 12
# **<u>Table 21. Second Stage Regressions Formal Labor Markets: Household Head and Spouse</u>**

||**Worked for a sal**<br>**months (1 if he/**<br>**sal**|**ary in the past 12**<br>**she worked for a**<br>**ary)**|**Tried to find a job in**<br>**(1 if he/she tri**|**the past 12 months**<br>**ed to find a job)**|
|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|
|Violent Shock|0.0237|0.1641***|0.0044|0.0555|
||(0.0156)|(0.0600)|(0.0148)|(0.0542)|
|Rural District Price Index|0.0073*|0.0090|-0.0026|-0.0020|
||(0.0041)|(0.0064)|(0.0034)|(0.0041)|
|Litk I Sh f Ttl Ailtl I|0.0481**|0.0580***|-0.0251|-0.0215|
|vesoc ncome as are o oa grcuura ncome|(0.0190)|(0.0210)|(0.0157)|(0.0158)|
|_Dummy_Participation in Goverment Program Familias in Acción (1|-0.0196|-0.0235*|0.0161|0.0146|
|if has acces to the program; 0 otherwise)|(0.0137)|(0.0141)|(0.0108)|(0.0109)|
|A|-0.0014***|-0.0017***|-0.0021***|-0.0022***|
|ge|(0.0005)|(0.0006)|(0.0004)|(0.0004)|
|Edi|0.0096***|0.0092***|-0.0002|-0.0004|
|ucaton (years)|(0.0023)|(0.0023)|(0.0015)|(0.0015)|
||0.0543***|0.0597***|-0.0242*|-0.0223|
|_Dummy_Female Household Head (1 female head of household , 0 otherwise)|(0.0185)|(0.0182)|(0.0136)|(0.0136)|
|_Dumm_Wealth Index Quintile 2|-0.0173|-0.0220|-0.0351**|-0.0368**|
|_y_|(0.0167)|(0.0168)|(0.0170)|(0.0172)|
|_Dumm_Wealth Index Quintile 3|0.0091|0.0045|-0.0674***|-0.0691***|
|_y_|(0.0213)|(0.0220)|(0.0165)|(0.0166)|
|_D_Wlth Ind Qintil 4|0.0103|0.0042|-0.0634***|-0.0657***|
|_ummy_ea ex ue|(0.0197)|(0.0207)|(0.0175)|(0.0178)|
|_D_Wlh Id iil 5|-0.0253|-0.0345|-0.0907***|-0.0941***|
|_ummy_eat nex Qunte|(0.0213)|(0.0220)|(0.0152)|(0.0156)|
||0.0097|0.0083|-0.0073|-0.0078|
|Number of children under 5 years of age|||||
||(0.0084)|(0.0088)|(0.0072)|(0.0071)|
||0.0166***|0.0174***|0.0091**|0.0094**|
|Number of children between 5 and 18 years of age|(0.0044)|(0.0045)|(0.0043)|(0.0044)|
|Number of members 65 or older|-0.0352***|-0.0380***|0.0004|-0.0006|
||(0.0107)|(0.0110)|(0.0093)|(0.0094)|
|Number of members of extended famil|-0.0004|0.0008|0.0020|0.0025|
|y|(0.0055)|(0.0055)|(0.0044)|(0.0045)|
|Constant|-0.5747|-0.6949|0.5604|0.4257|
||(0.4427)|(0.6481)|(0.3639)|(0.4211)|
|Observations|6,501|6,501|6,501|6,501|
|R-squared|0.0485|0.0485|0.0378|0.0378|
|Hansen Test||0.891||0.726|
|Estimated effect of a standard deviation increase in probability of violent<br>||0,118||0,046|
|shock (in standard deviations of dependent variable)|||||
|Cluster by community|||||
|Regional Fixed Effects|||||
|*** p<0.01, ** p<0.05, * p<0.1|||||

Source: CLS-WILL: first wave – household questionnaire.

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# **1 Introduction**

This paper aims at showing that labor supply decisions are often made taking into account other’s labor supply, due to the existence of strong comparison effects. It also goes one step further in showing that job search effort is affected by comparisons with others. The dataset used is the BHPS (British Household Panel Survey). It is a representative sample of the British population, from which labor market status and a composite measure of self-reported well-being (to proxy for utility) are used.

Three distinctive results are presented. First, it is shown that upon losing their job, individuals report a fall in their well-being. This fall is reduced when there are more unemployed in one’s comparison group (household and region), referred to as the ”Social Norm Effect”, as in Clark (2003). This effect persists in panel data estimations were the unobserved individual heterogeneity is controlled for. The second finding is that unemployment duration is affected by this social norm effect. The more an individual reports feeling hurt when losing his job -(the happiness difference)- the shortest will be his duration in unemployment. The happiness difference is a good predictor of the duration of unemployment, even after controlling for demographic characteristics also affecting duration. The third result shows that job search effort is itself dependent on the happiness difference. An individual searches with more intensity when he reports a large happiness drop when entering unemployment.

The implications of these results are twofold. First, they shed light on our understanding of job search effort. The results suggest that search effort is positively dependent on _Ve − Vu_ , the difference in well-being an individual reports between being employed and jobless. As the payoff from being employed rises (falls), the unemployed will search more (less). Second, they provide a labor-supply explanation of unemployment hysteresis. Due to comparison effects, when an individual loses his job, he feels less bad if there are more unemployed around him. His utility is hence affected by other’s employment status. This will reduce his search effort and increase his unemployment duration, affecting then the search behavior of others. In the case of an exogenous macroeconomic shock that reduces labor demand, our findings suggest that labor supply will also shift to the left, _increasing unemployment_ and causing hysteresis.

The remaining of the paper is organized as follows. Section II provides a review of the literature on subjective well-being (SWB henceforth) and labor market status. Section III describes the 16 waves of the BHPS. Section IV shows the first results of importance. It first presents the determinants of SWB, and then the social norm effect. Both pooled and panel data specifications are explained. Section V introduces the determinants of unemployment duration and search effort. Section VI concludes.

3
companies, many of which are ultimately controlled by either a wealthy family or a bank (see Prowse (1994)). This structure serves to increase the control of banks over firms through their equity ownership.

# Table 2

# Summary statistics of ownership concentration of large non-financial corporations

# Percentage of outstanding shares owned by the largest five shareholders

|Mean ...................|25.4|33.1|41.5|
|---|---|---|---|
|Median .........<br>..........|20.9|29.7|37.0|
|Standard deviation ..................|16.0|13.8|14.5|
|Minimum ............<br>........|1.3|10.9|15.0|
|Maximum ............<br>.......|87.1|85.0|89.6|
|Mean firm sizel||||
|(millions of US$, 1980) .........<br>|3,505|1,835|3,483|
|Mean firm size<sup>2</sup><br>(millions of US$, 1980) .........1|1,287|811|1,497|

I Measured by total assets. 2 Measured by market value of equity.

Samples: United States: 457 non-financial corporations in 1980.

Japan: 143 mining and manufacturing corporations in 1984.

Germany: 41 non-financial corporations in 1990.

Sources: For the United States and Japan, Prowse (1992); for Germany, Prowse (1993). Size data converted to US$, using 1980 average exchange rates and deflated by US consumer prices.

One major reason for the differences in the ownership of enterprises by banks are differences in the legal and regulatory environment in the three countries. Banks in the U.S. are simply prohibited from owning any stock on their own account, while bank holding companies cannot own more than 5 percent of the firm and their holdings must be passive. Japanese commercial banks are not prohibited from owning corporate stock, although they are subject to anti-monopoly regulations that until 1987 limited a single bank's holdings of a single firm's shares to 10 percent (the limit has since been lowered to _5_ percent). Banks in Germany can hold whatever share of equity they like in any nonfinancial firm, limited only by a number of prudential rules which do not appear to be particularly binding.

# _Bank-enterprise relationships_

There are likely similar differences between countries in the ownership concentration of debt claims on firms, although the available data is much more sparse. The greater reliance on securities markets than on intermediated markets for debt finance in the U.S. (illustrated in table 3) may be taken as a rough indicator that fragmentation of debt claims is higher there than in Japan or Germany. A second indicator may be differences in the regulatory limits on banks' exposures to individual customers--these limits are generally less binding in Germany and Japan than in the U.S.. For example, in the U.S., banks are limited to lending 15% of their capital to an individual borrower, whereas in Japan and Germany the limits are 30% and 50% respectively.<sup>5</sup> There are also legal and regulatory differences in the degree to which investors are permitted to take debt and equity stakes in _5_ See Borio (1990).

5
which is based on its tax return. This tax return therefore represents all the information the bank needs in order to determine its response to a request for a loan.

# **Consumption**

There are 18 different consumer categories, each of which has either urban or rural labor, initial allocations of the 5 capital types, and land. They also have initial allocations of financial assets; money, bonds, and foreign exchange. The 18 categories are taken from the SAM for Pakistan (SAMWALK 2010). They are given by:

1.  Urban quintile 1

2. Urban quintile 2 3. Urban other 4. Medium farm Sindh 5. Medium farm Punjab 6. Medium farm Other Pakistan 7. Small farm Sindh 8. Small farm Punjab 9. Small farm Other Pakistan 10. Landless Farmer Sindh 11. Landless Farmer Punjab 12. Landless Farmer Other Pakistan

- 13.Waged rural landless farmers  Sindh

14.Waged rural landless farmers Punjab

- 15.Waged rural landless farmers Other Pakistan

16.Rural non-farm quintile 1 17.Rural non-farm quintile 2

- 18.Rural non-farm other

The consumer classes have differing Cobb-Douglas demand functions with weights derived from the SAM consumption data. Endowments are similarly taken from the SAM. The consumers maximize intertemporal utility functions, which have as arguments the levels of consumption and leisure in each of the two periods. See Appendix 3 for the complete consumer’s optimization problem.

14
affected girls’ participation decisions. Girls could not attend as regularly as boys and were less likely to end up with job offers from their trainers. Overall, the experience led to more positive effects on self-reported well-being among male participants. These results support the conclusions of Duflo (2012)’s review of gender and development that women’s empowerment will require active and continuous policy commitment to equality in order to level the playing field.

Given the continued investments in skills development that we observe among trainees, it would be valuable to follow this sample up over a longer period to identify whether the additional human capital leads to improved labor market outcomes in the long run. In this context, an important shortcoming of our analysis is that the follow-up survey was conducted only 4 months after the completion of the training program (on average). However, conducting the follow-up quickly allowed us to track down many of the drop-outs, which was valuable.

22
fiscal policies. This is not surprising due to their heavy reliance on indirect taxation. Nonetheless, some low- and lower-middle-income countries apply fiscal policies more effectively, achieving both inequality and short-term poverty reduction. The findings for Cambodia suggest some fiscal system reforms are needed to improve its distributional effect, drawing on the international lessons for progressive fiscal policy in World Bank (2022b). Without reform, poorer households will continue paying more into the fiscal system than they receive from it in cash terms.

Cambodia must look to spend more and spend better to enhance the redistributive effect of the fiscal system. Higher transfers to poorer households and better targeted transfers would help improve the short-term poverty impact and cost-effectiveness of these transfers. Higher transfers will offset losses in purchasing power from indirect taxation, while improved identification of poor and vulnerable households ensures those most in need benefit. Some of these gains could be achieved by reallocating spending away from regressive subsidies such as electricity. For example, electricity subsidies cost 0.1 to 0.15 percent of GDP and do not benefit poorer households very much; if spent instead on targeted cash transfers, this would double the pre-pandemic budget. Further, maintaining some of the increased pandemic spending will bring Cambodia’s social assistance budget closer to regional and international levels while offsetting much of the short-term increase in poverty from fiscal policy. As a rough calculation, the poverty-reducing effects of Cambodia’s COVID-19 response, if sustained after the pandemic, would largely eliminate the short-term impact of fiscal policy on poverty.<sup>21</sup> Even if social assistance spending was not maintained at COVID-19 levels but sustained at half the level, the impact of fiscal policy on short-term poverty would put Cambodia closer to the middle of the upper-middle-income country range (Figure 24).

Figure 24: Impact of fiscal policy on short-term poverty in LMICs <!-- Start of picture text --> 6 Pre-COVID impact<br>5 Half COVID<br>4 COVID response* response*<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>Percentage point change -4<br><!-- End of picture text --> Cambodia could also spend more on health and education. Countries that see significant reductions in inequality usually do so through health and education spending. Of all the fiscal interventions modeled for Cambodia, spending on primary and secondary education contributed most to inequality reduction. Allocations to primary and secondary education to improve access and quality of services would be pro-

> 21 As World Bank (2022a) documents, Cambodia increased its social assistance spending in response to the COVID19 crisis from 0.1 percent to 0.7 percent of GDP between 2019 and 2020. The report estimates that this mitigated the poverty-increasing impact of the pandemic by 1.9 percentage points, which is applied to the 2019 CEQ results of this paper to roughly update Cambodia’s fiscal policy impact on short-term poverty. This is a rough approximation only.

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