rafmacalaba/datause-encoder-data huggingface dataset:
| POLICY RESEARCH WORKING PAPER 2716

# Summary findings

Existing evidence on the effect of foreign bank penetration on lending to small and medium-size enterprises is ambiguous. Case studies of developing counitries show that foreign banks lend less to such firms than domestic banks do. But cross-country studies find that foreign bank entry fosters competition and reduces interest rates, benefits that should extend to all firms.

Clarke, Cull, and Martinez Peria use data f^nom a large cross-country survey of enterprises to investi-.rte this issue. Their results suggest that foreign bank p2netration improves financing conditions (both the quantities of financing and the terms) for enterprises of all sizes, although it seems to benefit larger firms more.

This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to understand the impact of entry by foreign banks on domestic banking systems in developing countries. Copies of the paper **alre** available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3422, telephone 202-473-7644, fax 202-522-1155, email address psintimaboagyeCixworldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be c :>ntacted at gclarkeCiiworldbank.org, rcull(qworldbank.org, or mmartinezperiaCa worldbank.org. November 2001. (37 pages)

> _The Policy Research Wlorking Paper Series dissc "nates the find ngs of wvork inz progress to encourage the exchantge of idcas z,5out development issues. An objective of the series is to get the indinigs out quickly, even if the presentationis are less than fully polished. Thc papers carry the names 4f the authors and should be cited accordingly. The findings, inzterpretations, anaei concluisionis expressed il this paper are entirely those of the authors. They do not necessarily represent the ,ieue ot the W"orld B ntk, its Executive l)irectors, or the countries they represent._

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- 15 -

notably, the impact of the crisis on beef demand looks permanent given their demand estimation. This BSE effect will be taken into account in the following empirical analysis.

<!-- Start of picture text --> Figure 5. EU and U.S. Import Prices of Beef, 1990-2004<br>(In Euro per kilogram)<br>5.0<br>4.5 Extra-EU<br>beef import price<br>4.0<br>3.5<br>3.0<br>Intra-EU beef import<br>2.5<br>2.0<br>1.5<br>U.S. import price of<br>1.0 Australian and New Zealand beef<br>0.5<br>0.0<br>Sources: Intra- and Extra-EU Trade Statistics ; and WEO database.<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Third, Sub-Saharan Africa has repeatedly suffered from severe droughts over the past decades. As occurred periodically, most beef exporters in the region have reduced cattle processing due to the recent draught.

Finally, there is an alleged notion that lack of adequate processing, transport and telecommunications infrastructure would undermine beef export performance of Africa. Abattoirs are obviously part of important infrastructure.<sup>24</sup> Poor road network would be a crucial bottleneck for improving livestock productivity in the region (Buys _et al_ ., 2006; Pinstrup-Anderson, Shimokawa, 2006; Williams _et al_ ., 2006; Broadman, 2007). Moreover, without appropriate infrastructure for communication and education, it would be difficult to develop an effective marketing and production system. Marketing inefficiency may be prominent, particularly when state-owned enterprises interfere with trading activities.<sup>25</sup>

> 24 One of the common problems for cattle farmers in Africa is that there are a limited number of abattoirs in a country; farmers have to pay high costs to deliver their cattle to an abattoir.

> 25 In Botswana, a state-owned enterprise, Botswana Meat Commission, which has exclusive responsibility for marketing beef products abroad, has been a loss-maker. Its untimely cattle collection and slow payment to farmers may be complicating inefficiency in livestock production and export.
Policy Research Working Paper 11026

# **Abstract**

Longitudinal surveys are an invaluable source for analyzing the current state of and changes in human populations over time. However, maintaining the accuracy of estimates from a panel sample becomes more difficult as the length of the panel survey increases. Key concerns are the lack of sample representativeness, due to the sample erosion caused by deaths and movers and the impact of new births, and migration flows. Moreover, sample fatigue introduces an increasing measurement error. Correct design, implementation, and use of a panel survey considers a set of methods to deal with these problems at different stages of the statistical process: the sampling design, the data collection, and the estimation. This paper focuses on the case of panels with a rotating sample design. This case represents a powerful hybrid solution for facing the impact of panel dynamics on sample representativeness. Empirically, the paper focuses on the estimation procedures. Using data from the Uganda National Panel Survey, the longest Living Standards Measurement Study panel survey, it experimentally evaluates the suggested technique. In summary, the findings show that the calibrated generalized weight share method base estimator yields individual-level statistics that appear to be more accurate than those produced by the current Uganda National Panel Survey estimator. Additionally, the calibrated generalized weight share method base cross-sectional estimates on the transition matrix show a generally higher degree of stability when the sample is changed compared to the current Uganda National Panel Survey estimates.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at gponzini@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
telecommunications, water, and waste. Reforms in these infrastructure and utility sectors have worsened inequality. They appear to benefit mostly those in the top two deciles ( _i.e_ ., the richest 20 percent) of the population. Nearly 70 percent of the population has seen their share of consumption or income decline as a result of infrastructure privatization and fee-changes. This outcome may be partly explained by the fact that infrastructure privatization meant the abolition of monolithic government ownership of these structures that used to provide at times inefficient, yet subsidized and/or free, services to their citizens. The sizes of the two strongly significant effects (pro-poor small scale privatization and pro-rich infrastructure reform) are such that they almost exactly balance each other out: 1 point increase in the respective EBRD indexes produces about the same absolute effect.<sup>16</sup> The rest of the EBRD transition indicators played a more or less nondiscriminatory role in the evolution of inequality. Enforcement actions to reduce abuse of market power and to promote a competitive business environment appear to favor those in the middle income classes, with no significant effect on the poorest and the richest. Improvement in the banking laws and regulations, and financial deepening also benefit more those in the middle and upper income brackets. There is some indication that enterprise restructuring tends to favor the very top income class to the detriment of the middle. If job losses, following upon restructuring, are concentrated among middle classes, this is not surprising. The rest of the EBRD components are inequality-neutral. But in some cases, that neutrality is quite remarkable: thus price liberalization, and foreign trade and exchange rate liberalization, frequently regarded as anti-poor, at least in the short term, appear to have an entirely neutral effect on income distribution.

> 16 Of course, this is merely of an econometric rather  than real relevance since one point increase in EBRD index may involve vastly different policies in the case of infrastructure reform than in the case of smallscale privatization.

22
# 10. Appendix

Table 2 Summary statistics for comparable variables used in model to impute consumption, by survey

||(1)|(2)|(3)|
|---|---|---|---|
||2018/19 NLSS|2018/19 GHS|Difference<br>2018/19|
|North-Central|14.07%|14.07%||
|North-East|8.28%|8.28%||
|North-West|19.51%|19.51%||
|South-East|13.13%|13.13%||
|South-South|17.90%|17.90%||
|Gender|81.17%|80.17%|-1.00|
|Dependency Ratio|42.44%|43.09%|0.66|
|<br>Employed: waged|19.76%|17.21%|-2.55|
|Employed: non-farm|37.18%|39.74%|2.57|
|Main floor material:cement|70.30%|71.39%|1.09|
|Main cooking fuel: wood|59.06%|60.57%|1.51|
|No toilet|25.07%|29.03%|3.96|
|Imported rice|43.85%|47.13%|3.28|
|Beef|45.35%|43.91%|-1.44|
|Fish-fresh|18.14%|15.71%|-2.43|
|Recharge cards|85.13%|85.07%|-0.06|
|Air Conditioner|2.57%|1.68%|-0.89|
|Washing Machine|2.16%|1.96%|-0.20|
|Cars and other vehicles|8.47%|9.99%|1.52|
|Generator|24.96%|25.49%|0.52|
|Microwave|2.47%|1.74%|-0.73|
|TV Set|48.03%|47.73%|-0.29|
|Computer|4.71%|4.17%|-0.55|
|Note: the table shows the average value of non-monetary in<br>to-survey imputations. Data are from the 2018/19 NLSS and2<br>between two visits (post-planting and post-harvesting). For 2<br>NLSS zone population shares and ensure comparability.|dicators used in the con<br>018/19 GHS. For GHS d<br>018/19 GHS zone weigh|sumption model for th<br>ata the average reflect<br>ts are adjusted to mat|e purpose of survey-<br>s the average value<br>ch 2018/19 official|

Table 3 Sectoral GDP growth rates 2009-2019

||GDP, trillions<br>(Constant LCU)|Sectoral GDP|, trillions (Cons|tant LCU)|Sectoral|GDP growth|rates|
|---|---|---|---|---|---|---|---|
||Total|Agriculture|Industry|Services|Agriculture|Industry|Services|
|2009|54.6|13.0|13.8|27.7|5.9|2.5|12.4|
|2010|57.5|13.4|15.0|29.1|5.8|5.2|12.9|
|2011|59.9|14.3|15.4|30.2|2.9|8.4|4.9|
|2012|63.2|14.8|15.7|32.8|6.7|2.4|4.0|
|2013|67.2|15.4|16.7|35.0|2.9|2.2|8.4|
|2014|69.0|16.0|16.4|36.7|4.3|6.8|6.8|
|2015|67.9|16.6|14.9|36.4|3.7|-2.2|4.8|
|2016|68.5|17.2|15.2|36.1|4.1|-8.9|-0.8|
|2017|69.8|17.5|15.5|36.7|3.4|2.1|-0.9|
|2018|71.4|18.0|15.9|37.5|2.1|1.9|1.8|
|2019|70.0|18.3|15.0|36.7|2.4|2.3|2.2|

Note: the table shows sectoral GDP data. Data in the last three columns on sectoral GDP growth rates are used in the backcasting exercise. Source: World Bank MFM-Tool.

29
information on the temporal evolution of the opportunity set.

The OGIC, in both the individual and the type versions, can be used to rank different growth episodes. Analogously with the literature on the standard GIC, we can apply first-order dominance criteria based on the OGIC.<sup>15</sup> First-order dominance implies that the OGIC of a growth spell is everywhere above that of another.

However, the two approaches (individual and type OGIC) are generally not equivalent, and they can generate a different ranking of growth processes. In fact, beyond their interpretation and the fact that they can be used to investigate different aspects of the relationship between economic growth and EOp, the differences between the individual and the type OGIC are mainly due to demographic and reranking issues. The following remark makes this point clear.

**Remark 1.** _Let Yt_<sup>_A_</sup> _and Yt_<sup>_B_</sup> _be two initial distributions of income, and let G_<sup>_A_</sup> _and G_<sup>_B_</sup> _be two different growth processes taking place, respectively, on Yt_<sup>_A_</sup> _and Yt_<sup>_B_</sup> _and generating, respectively, two final distributions of income, Yt_<sup>_A_</sup> +1<sup>_andY_</sup> _t_<sup>_B_</sup> +1<sup>_._</sup> _Moreover, let nA and nB be the number of types, respectively, in Yt_<sup>_A_</sup> _and Yt_<sup>_B_</sup> _and mAi and mBi be the number of individuals in each type i_ = 1 _, ..., n, respectively, in Yt_<sup>_A_</sup> _and Yt_<sup>_B._</sup> _If_ ( _i_ ) _nAt_ = _nBt, ∀t_ = 1 _, ..., T ,_ ( _ii_ ) _mAit_ = _mBit ∀i ∈{_ 1 _, ..., n} , ∀t_ = 1 _, ..., T ,_ ( _iii_ ) _no reranking of types, then g_  ̃<sup>_Ao_�</sup> _n_<sup>_<u>i</u>_</sup> � _⪰ g_  ̃<sup>_Bo_�</sup> _n_<sup>_<u>i</u>_</sup> � _∀i ∈{_ 1 _, ..., n} if and only if gY_<sup>_Aos_</sup> � _Nj_ � _⪰ gY_<sup>_Bos_</sup> � _Nj_ � _∀j ∈{_ 1 _, ..., N }._

**Proof.** See appendix.

This remark establishes that when the two distributions have, at each point in time ( _i_ ), the same number of types and ( _ii_ ) the same type-specific population size, and when ( _iii_ ) types keep their relative position in the type mean income distribution over time, ranking income distributions according to the individual OGIC is equivalent to ranking income distributions according the type OGIC. Because conditions ( _i_ ) and ( _ii_ ) basically impose restrictions on the types’ demography and condition ( _iii_ ) imposes restrictions on the rank of the types, it is clear that possible differences in the ordering provided by the two OGICs are determined by variations in the type’s population shares, between the two distributions and the two periods compared, and by the reranking of types over time.

> 15For a normative justification of these dominance conditions based on a rank-dependent social welfare function, see the working paper version of the paper: Peragine et al. (2011).

14
Nairobi, we obtain the distribution of employment using data from Henderson et al. (2018), where for each grid square we know the footprint and height of every building in Nairobi in 2015 – from aerial photo and Lidar data – and can calculate building volume. We match these buildings with land use maps, before taking total employment of the city and smearing it into grid squares according to each grid squares share in total volume of nonresidential buildings in Nairobi. The alternative would be to smear it into commercial and industrial buildings, ignoring public buildings. Crucially, unlike Landscan we do not need to base smearing on inferences from satellite images of what uses buildings have; instead, we know the use and the volume fairly accurately.

For each city we create a measure of the ambient population according to the following equation: We base our replication of the ambient population just on places of work and residence, where we assume for 14 hours of a day (nocturnal) all people are in their grid square of residence to sleep, eat, and recreate. For 10 hours a day, we add in the employment in the grid square, allowing people time to work, hangout, and finish commuting. We then add in the non-working population of the grid square assuming they remain in that square kilometer and subtract out the resident workers (since we have already counted employment). If everyone works in their grid, then we just have total grid square population; but, for downtown grid squares where few people live, most have replication numbers from employment. We make no allowance for the time people are on roads or shopping outside the grid square. We have no information on which to base such inferences, especially in a context where so many people commute by walking.

In Figure 1 for Kampala, we show 4 items. As noted above, in the upper left panel of each figure is the population distribution over space and on the right upper panel is the employment distribution. In the bottom panel, on the left, we have Landscan numbers; and on the right, we have our replication numbers. For Kampala, we see the overall monotonicity of the city. Although it is hard to see, the very low bar population grid squares near the center are to some degree filled in by where employment spikes. The bottom 7
11 ages is common in Ethiopia, see Pathfinder (2006). Completed fertility tends to be the characteristic of families with more children, that is, those more likely to be closer to the end of their fertility cycle. Undoubtedly the best way to deal with this issue is to confine the samples to those with completed fertility, that is, families with the mother aged above, say, 35. Unfortunately, this would severely reduce the sample size. A compromise employed here is to exclude at least some with mothers at an early phase of their fertility, e.g. mother younger than 20.

Four categories of expenditure appear reasonable candidates for adult goods in Ethiopia: tobacco, coffee, adult clothing, and adult personal services and personal effects, ‘personal services’ for short. Since the latter provides consistently the strongest evidence for child gender bias in Ethiopia, I note that items in this group are made up of shavingrelated goods and services, in addition to after-shave, perfumes, hand-bags and wallets, sticks and wig<sup>11</sup> . Table 2 shows the incidence of purchase/non-purchase for each group in the survey, ranging from just under a quarter to well over a third of the household. As such, the percentages of non-purchase are not unlike those expected of some common adult goods; see Subramanian and Deaton (1991) for India, Koohi-Kamali (2007) for Iran. Finally, table 3 gives the mean and the standard deviation of the main variables of equations (1) and (2) for the main, all-country (covering both urban and rural areas) sample of nuclear households consisting solely of the household head, the spouse and their biological, under 15, children.

Child gender has different dimensions worth examining because they are of interest to public policy, or perhaps of particular importance for understanding the issue in the Ethiopian context. In this regard, I shall examine two additional issues below. First is the sensibility of the precise age definition of a child. This is an important issue on its own right, but, in addition, the influence of child labor earnings on internal allocation would be hard to isolate with the model employed here. Moreover, as there may be some child consumption of adult goods at the child-adult age boundary, the evidence with a sample of lower child age definition provides a robustness check on whether the selected

> <sup>11</sup> The number of observations on positive purchase of alcohol amount to just over 100 households in the entire sample. Jewellery was also examined, but it seemed an uninformative candidate for an adult good in Ethiopia, perhaps because its expenditure constitutes a form of investment.
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37
have features devised to limit moral hazard. This is the case of the Emergency Financing Mechanism introduced after the Mexican crisis, of the IMF's Supplemental Reserve Facility (SRF) established in 1997, of the Contingent Credit Lines (CCL) introduced in April 1999, and of the supply of guarantee on the part of the World Bank.

In addition to a large and more liquid supply of finds from the IFIs, it has been recognized that the resources to be deployed in a crisis should in the future rely on the contribution of the private sector to a larger extent. A source of private funds may be untapped by often debated changes in bond contracts, allowing qualified majorities to modify the bond covenants in order to facilitate bond roll-over in the event of a crisis. This is much in the same spirit that led in the past to rolling over syndicated bank loans. An additional source of funds has been pioneered by Argentina, South Africa, and Mexico, who have entered a financial contract with consortiums of foreign commercial banks for the provision of liquidity in times of crisis.

Once the amount of resources available for liquidity provision has been settled, there is the need to establish guidelines for the execution of assistance policies. What are the features of an International Lender of Last Resort and how effectively can it address the issue of preventing and managing the occurrence of episodes of financial contagion? We shall consider this question, first, from the perspective of liquidity creation, and then looking at the potential implications for moral hazard.

Our theoretical discussion implies that, to the extent that liquid resources are sufficient to fully meet a debtor position, a crisis can be effectively eliminated. This implication can be illustrated with reference to the model of Section 2. Assume that an intemational institution is committed to step in if the new debt auction is unsuccessful, purchasing all the unsold bonds at the prevailing world retum. Referring to the graphical representation of equilibria in Chart 1, we observe that in this case the bad outcome possibility effectively disappears - and with it the OG curve in Chart 1. Default will obtain with probability zero on the left hand side of the schedule OH and with probability I on the right hand side. Both the crisis zone and the eventuality of contagion are effectively dispelled by the promise of a full financing in the event of a crisis.

Notably, it turns out to be crucial from the IFI's perspective to be able to properly assess the weakness of the economy before lending takes place. In the simplified setting 29