rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3660

# **Do Incumbents Manipulate Access to Finance During Banking Crises?**

Erik Feijen*

## **Abstract**

This paper tests the hypothesis that during systemic banking crises, access to finance is opportunistically tightened by incumbents to eliminate or weaken competition from mainly young firms. We find this to be especially true in more corrupt countries. To do so, we employ a methodology similar to Rajan and Zingales (1998) on three digit manufacturing industry-level data provided by the United Nations Statistics Division for about 15 developed and developing countries in over 20 industries on average. We show that price-cost margins in externally _more_ financially dependent industries are _higher_ during crisis than in externally _less_ dependent industries in countries with _higher_ levels of corruption. We find the opposite relationship for the change in the industry-level number of establishments during a crisis. The results withstand an array of robustness checks, including using different indices of corruption, different controls, and robust estimation techniques.

JEL Classifications: D73, G1, G2, G3, and P48.

Keywords: Access to Finance; Systemic Banking Crises; Corruption.

## World Bank Policy Research Working Paper 3660, July 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._

* University of Amsterdam and World Bank. Email: <u>e.h.b.feijen@uva.nl. I thank the World Bank’s</u> Financial Sector Operations and Policy Department for its hospitality while
# **1 Introduction**

Traditionally, the study of poverty dynamics requires tracking the same individuals or households over time. However, in many developing countries, panel data is scarce, which prevents analysts from observing the economic trajectories of individuals or households over an extended period of time.

Following the path opened by Deaton (1985), papers by Bourguignon, Goh, and Kim (2004), and Guell and Hu (2006) explored the use of synthetic panels at the household level. However, these two approaches require certain assumptions that may not be easily satisfied in available cross-sectional data. The former method requires at least three rounds of cross-sectional data, and assumes a first-order auto-regression process where past household or individual incomes (earnings) can affect present outcomes; the latter is exclusively restricted to duration analysis. To overcome these limitations, Dang et al. (2014) constructed synthetic panels from as few as two rounds of household-level crosssectional data, providing lower-bound and upper-bound estimates of poverty transitions. More recently, Dang and Lanjouw (2023) proposed a method that delivers point estimates of poverty transition probabilities (households shifting in and out of poverty, or preserving their status) in the absence of longitudinal data.

This paper takes advantage of these new developments, as well as some critiques that have recently been raised regarding some aspects of the methodology (e.g., Herault and Jenkins, 2019; Garcés‐Urzainqui, Lanjouw, and Rongen. 2021, Colgan, 2023), to delve deeper into the characteristics of poverty dynamics in Bhutan during the five-year period from 2017 to 2022. According to Amendola et al. (2023), during this time the stark decrease of poverty in Bhutan at the national and sub-national levels seems to be linked mainly to redistributive processes in the monetary metric of expenses, rather than to economic growth. The fact that the key driver of poverty reduction is a reshuffling of households along the distribution curve, rather than a movement of the curve itself, justifies the need for tools
Figure 19: Number of users of public education and health

# A. Distribution of publicly enrolled students

B. Percent of households visiting a public health facility by decile <!-- Start of picture text --> 70% 25%<br>60%<br>20%<br>50%<br>40% 15%<br>30%<br>10%<br>20%<br>5%<br>10%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Market income decile Market income decile<br>Preschool Primary Secondary Tertiary<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Figure 20: Distribution of in-kind transfers by decile <!-- Start of picture text --> 50B<br>40B<br>30B<br>20B<br>10B<br>0B<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Education Health<br>Riel (billions)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Most of the electricity subsidies accrue to non-poor households. Non-poor households are more likely to be connected to the electricity grid and thus consume more electricity. The poor are more likely to live in neighborhoods without access to an electricity network, and even where electricity is present, they often cannot afford connection or monthly fees. Even if they can afford the connection fees, their electricity consumption tends to be low. Overall, the implicit subsidy embedded in the tariff structure is regressive.

27
covered 7,995 households, accounting for 24% of the total sample. But this module covered approximately 24% of households in each region. Similarly, the Dietary (Individual) module covered even fewer households (4,213), accounting for 13% of total households and 53% of households covered in the Dietary (Household) module. Ultimately, the overall demographic characteristics of those covered in this module were not significantly different from those of the full sample. A visualization of the final sample selection process is presented in Figure 1.

_Figure 1. Final Sample Selection Process_ <!-- Start of picture text --> Full Sample<br>33,400 HHs / 160,888<br>individuals<br>Dietary -<br>Household<br>Module<br>7,995 HHs / 40,922<br>Individuals<br>Dietary -<br>Individual<br>Module<br>4,213 HHs / 18,472<br>individuals<br><!-- End of picture text --> We rely on the Family Income and Expenditure Survey (FIES) 2015 for price data. As only _quantities_ of food group consumption were available in NNS 2013, food expenditures were imputed from the FIES 2015 from the Philippine Statistical Authority (PSA, 2020). These expenditures were used to derive prices (or unit values) of food groups surveyed in NNS 2013. More details on this process are in the subsequent section. However, as prices were in 2015, imputed food expenditures were adjusted to 2013 using the official CPI retrieved from the Philippine Statistical Authority. The CPI for 11 food groups in 17 regions was determined for both June 2013 and June 2015. Then CPI deflators were calculated and imputed food expenditures were determined for each food group in the appropriate region by their corresponding CPI deflators.<sup>1</sup>

> 1 Food groups available for CPIs were not perfectly matched with food groups in NNS 2013
# _Robustness check: Limiting the sample to countries with a STEP survey_

To assess if the imputation of task scores to countries beyond those covered by a STEP survey affects the results, we conduct a robustness check where we estimate the main equation using the original sample of countries with a STEP survey. Given that the number of observations is significantly smaller, we estimate more parsimonious specifications by replacing year dummy variables with decade dummy variables. In addition, we exclude GDP per capita (both in levels and changes) as control variables in the full model.

As seen in Table 10, the estimated coefficients associated with internet use and exports have in general the same signs as those of Table 5, although the latter are no longer statistically significant. Increased internet use is associated with increases (decreases) in the non-routine (routine) task intensity of jobs. Several other coefficients become statistically significant in this sample, but the number of observations is too small to draw conclusions.

**Table 10. Robustness check: Restricting the sample to countries with a STEP survey.**

||(1)|(2)|(3)<br>(4)|(5)<br>(6)|(7)<br>(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|
||Non‐routin|e analytical|Non‐routine interpersonal|Routine cognitive|Routine Manual|Non‐rou|tine manual|
||STEP‐|sample|STEP‐sample|STEP‐sample|STEP‐sample|STEP|‐sample|
|GDP (log), change|‐0.0187||0.00405|‐0.0151|0.0391|0.0117||
||(0.0314)||(0.0195)|(0
The study exploits this feature of the data to situate the current use of biogas within the overall energy mix in this part of rural

> continues. The smallholder digesters may also be filled with manure from the larger farms, as has been reported on a couple of occasions during our field visits.

> 10 Two follow up panel surveys are planned in the fall of 2011 and 2013 respectively.

> 11 The selected counties are Houqiuxian, Ningguoxian, and Taihuxian in Anhui province; Jjianjinqiu, Wanzhouqiu, and yungyianxian in Chongqing; Xingan, Pinglo, and Fangcheng in Guangxi; Lensuitangqiu, Wugangshi, and Yongdingqiu in Hunan; and Enshixian, Janshi, and Xiuenxian in Hubei.

> 12 Slightly fewer villages were selected in Guangxi Autonomous Region.
regional analysis before the pandemic reveals that Santa Cruz had the highest employment quality level, reaching 0.54 throughout the entire period. This value surpasses the national average and other departments, and even after the lifting of pandemic lockdown measures, job quality in Santa Cruz remained unchanged. Regions with similar worker participation, such as La Paz and Cochabamba, reported job quality levels of 0.42 and 0.45, respectively, placing them at the average national level. Conversely, Potosi and Chuquisaca had low job quality, with levels below the national average. During the pandemic, the quality of jobs in Potosi decreased by almost 10 points, making it the region with the lowest job quality.

In addition to this analysis, Figure 2a illustrates the distribution of job quality among regions by comparing two different periods: 2011 and 2019. The analysis based on the full 2011 household survey shows that the llanos orientales (Santa Cruz, Pando, and Beni) had higher job quality, while the rest of the departments had an average Job Quality Index (JQI) of 0.44. Comparing this situation to the 2019 one (see Figure 2b), the pattern seems to remain the same, with the exceptions of Oruro, which increased its job quality, and Cochabamba, which experienced the opposite change. In summary, these regional disparities remained relatively similar between 2011 and 2019. These findings align with the distribution of salaried workers in medium and large companies, which is higher in Santa Cruz and lower in the southern departments of Potosi and Chuquisaca.

Table 9: Average Job Quality between 2011-2021, by departments

|||||De|partments||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Chuquisac|(2)<br>a La Paz|(3)<br>Cochabamba|(4
constructed using Chinese regional input-output tables, but this can only be done at the provincial level, meaning that crucially, somewhat arbitrary allocation rules then need to be adopted to calculate RMA at the prefectural level. In our context, these rules would involve assuming, for example, that _G U_ and _G R_ are constant across regions within provinces (Hering and Poncet, 2010a, p 665). Second, and perhaps more importantly, the provincial input-output tables do not allow for an easy distinction to be made between urban and rural goods. This makes it difficult to calculate separate measures of RMA for the urban and rural sectors.

17
at the very early career stage, formal employment offers substantial training and learning opportunities that are associated with wage increases. Importantly, when workers switch between formal jobs they do not experience a loss in wages, suggesting that the rapid wage increase in formal employment is at least in part due to a gain in general rather than firmspecific skills. The wage incentive thus indirectly serves as a subsidy for workers to acquire human capital.

> 7While other wage subsidy programs also focus on youths, they do not administer the program before participants graduate. This is important as most graduates find employment shortly after graduating. At the same time it raises concerns about unintended consequences, e.g. with regard to educational decisions. 8 Indeed, one explanation for the muted effects of existing programs (e.g. Van der Klaauw and Van Ours (2013)) is that beneficiaries receive the bonus only after they hold a job for a certain period of time.

> 9Gains in statistical power from panel data are particularly large for outcomes with low autocorrelation, e.g. due to high job turnover rates (McKenzie, 2012). Our study is thus able to estimate employment effects more precisely than existing wage subsidy studies.

6
investors would not have participated in the loan without MDB involvement, MDB participation crowds in private investment and thus provides financial additionality.

Because these are large loans, borrowers (particularly those in emerging markets and developing economies) typically find syndicated loans to be less costly than raising a similar amount of funding through bond issuance and multiple bilateral loan agreements (Gurara et al., 2020; Santos and Winton, 2008; Godlewski and Weill, 2008).<sup>10</sup> They can also provide finance to firms that are unable to tap bond markets at all and, more generally, can help diversify firms’ sources of external finance. For private creditors, participation in a syndicate with an MDB allows them to generate fee income, diversify their exposures to specific borrowers and countries, and make loans in markets where they lack origination capabilities (Sufi 2007; Haselmann and Wachtel, 2011).

Given the advantages that syndicated loans offer to borrowers and lenders, it is not surprising that their use is widespread. Based on a dataset of roughly 20,000 syndicated loans to emerging markets and developing economies originated from 1994 to 2015, annual volumes grew from about $100 billion USD (2009) in the late 1990s to hover around $200 billion USD (2009) from 2006 to 2014, despite a dip during the global financial crisis (GFC) in 2008-2009 (Gurara, Presbitero, and Sarmiento, 2020). Within that sample, about 10 percent of the loan deals had at least one MDB in the loan syndicate. While these numbers are large, they fall far short of the trillions of dollars in annual financing that sources estimate are needed to achieve the SDGs.<sup>11</sup>

> 10 For one of the most comprehensive samples used to study syndicated loans, the median loan size was $65 million 2009 USD (Gurara, Presbitero, and Sarmiento, 2020).

> 11 See estimates listed in footnote 1 of this paper.

24
- 6 -

<!-- Start of picture text --> Figure 1. Road density in Nepal<br>Road Density per 100 km2 Road Density per 1000 population<br>0.0 - 2.8 0.00 - 0.10<br>2.9 - 6.9 0.11 - 0.32<br>7.0 - 11.5 0.33 - 0.52<br>11.6 - 19.3 0.53 - 0.71<br>19.4 - 29.4 0.72 - 0.90<br>29.5 - 57.9 0.91 - 1.10<br>58.0 - 87.5 1.11 - 1.38<br>87.6 - 205.8 1.39 - 2.23<br>. /<br><!-- End of picture text --> Source: Central Bureau of Statistics, Nepal (2004).

Recognizing the issues in the transport sector, the government has been making efforts to improve the rural accessibility by upgrading dry-season-only roads to all-season standards and providing structures, gravel surface and Otta seal, which is a low-cost paving option (World Bank, 2005; 2007). Some of the impacts have already been observed. An interim survey of the project indicates that the people’s mobility increased by more than 20 percent and that their travel time declined dramatically from 2.6 hours to 32 minutes on average (World Bank, 2009).

However, resources available for infrastructure development continue to be limited, as in other developing countries.<sup>1</sup> Nepal spent $105 million or 1.2 percent of GDP for road development (World Bank, 2005). This is more or less equally comparable with regional countries, such as Bangladesh spending 0.7 percent of GDP on transport infrastructure and India 1.4 percent. But
million to 4 million jobs every year to absorb new entrants into the labor market and reduce poverty (World Bank 2018).

This study provides an analysis of the labor market and jobs in urban Kinshasa—the capital of DRC. This focus on Kinshasa is predicated by the fact that it has vast potential to serve as a growth engine for job creation and poverty reduction in the country. Kinshasa is on its way to becoming the largest megacity in Africa over the next 25 to 30 years, registering an influx of 400,000 new inhabitants every year. Thus, the future of economic growth and poverty eradication rests upon the city’s ability to absorb these new entrants and provide them with access to jobs, more importantly, quality jobs. In particular, this study seeks to answer the following questions:

- (a) What are the labor supply and employment patterns in Kinshasa? And who is excluded from the labor market, either being inactive, unemployed, or underemployed?

- (b) What types of jobs, in terms of the economic sectors, informality, and self-employment versus wage, exist in Kinshasa? And where do they spatially concentrate in the city?

- (c) How is the labor market status related to neighborhoods and location characteristics?

To answer these questions, this paper relies primarily on a recently conducted household survey in Kinshasa, and is supplemented with other existing surveys and spatial data sets. The findings are summarized as follows. First, female and young workers are more likely to experience unemployment and underemployment than other workers in Kinshasa. The policy needs to support female and young workers by removing barriers—such as discrimination against them—to their accessing better jobs. Second, the availability of good-quality jobs is still limited in Kinshasa. The majority of existing jobs are low-productive, informal self-employment jobs. Third, in addition to 2
For purposes of LR, Germany relies on public valuation boards, or land valuation boards. These valuation boards cover specific areas (e.g. Berlin, Hamburg, Munich, Dresden, etc.), comprised 10 to 20 members and number around 1500 across Germany (Kertscher 2004). These members represent expertise from public survey departments and private sector knowledge on real estate and land valuation (Seidel 2006). Land valuation boards are entrusted to collect and maintain purchase price data; publish periodic valuation trend reports; write and provide valuation reports when requested by the public or private sector, or courts; and, generally assess property values and levels of compensation. These boards manage the process of valuation and provide oversight and accountability to the valuation process. This stresses the point made before that LR also requires efforts in terms of building institutions. In this case, institutions that govern land valuation for LR.

The existence of strong institutions allows Germany to use redistribution of land by relative value. Land valuation methods are outlined in the federal law, and include the sales comparison approach, the income, and the cost approach. For LR purposes, and to generally value land, the comparison approach is the most commonly used.

Purchase price data is critical to carrying out market comparison valuation. Germany’s database on purchase prices is a record of the ground value of all land transfers. Details on land plots included in the database are size of lot, type of use, location, date, etc. This information is available to the public for a fee (Seidel 2006). The database has been digitally stored since the 1980s, which allows for more complex computational valuation methods on large quantities of data, such as multiple regression analysis. By the 1990s, valuation committees began to georeferenced these data and linked it to geographical information systems (GIS) (Kertscher 2004). The evolution of the management of price data enables Germany to explore methods to increase transparency and ensure that data can be analyzed in the most accurate
similar to the annual per capita HFCE growth rates, the poverty rates derived using these two growth rates are fairly similar. The set of estimates employing the growth in per capita GDP are reported in panel B of Table 5.

Figure 5 presents the trends in national poverty for the years 2012 to 2018. These trends are reported for two series presented in Table 5– namely, (a) the trend using official per capita HFCE growth with the 0.67 pass-through rate and the 95 percent confidence interval (see row 1 of Table 2, panel A); and (b) the trend applying a downward adjustment of 2.5 percentage points to the per capita HFCE growth rate with a 0.67 pass-through rate. As highlighted in the figure, the changes in growth rates matter for the poverty rates. A downward adjustment of 2.5 percentage points of growth rates increases the national poverty rate by 1.4 percentage points, which translates to 18 million more people pushed into extreme poverty.

# **5. Summary and Conclusion**

This paper is an attempt to provide up-to-date information on poverty in India in the absence of regular data from the Consumption Expenditure Survey (CES). Most of the alternative data sources

> 33 Note that the 95% confidence interval is symmetric around the pass-through rate. However, when the pass-through rate and its 95% interval is mapped into the poverty rates, the confidence band around the poverty estimates are not symmetric. This is because the poverty estimates depend on the density around the poverty line.

> 34 Using the South Asia consumption-specific pass-through rate of 0.652 Table A.5 with a 95% confidence interval of [0.307, 0.998] yields a national poverty rate of 10.49 percent (with 95% confidence interval between [8.84, 12.43]), a rural poverty rate of 12.13 percent (with 95% confidence interval between [10.21, 14.28]),