rafmacalaba/datause-encoder-data huggingface dataset:
weaving industries. The Cotton Advisory B3oard's role is to estimate the supply, demand and opening stock of cotton crops every year. The members include the Textile Commissioner as chairman, several joint secretaries of the Ministry of Textiles ancl of the Ministry of Agriculture, state government officials in charge of agriculture, and other organizations representing all the sectors that are involved in cotton trading either directly or indirectly.

As far as cotton trading is concerned, the role of the Ministry of Agriculture is limited to suggesting the minimum support price that cotton farmlers are guaranteed to receive. The Commission for Agriculture Costs and Prices records the prices of basic cotton varieties every year. If the market price of cotton is below the minimum support price, the Cotton Corporation of India (CCI) must enter the cotton market to purchase cotton in the domestic cotton market. However, in practice, the minimum support price has been set so low - usually less than 70 per cent of the quoted market price - that it has been touched only once in the past twenty five years.

# **_Methods of Crop Estimation_**

The members of the Cotton Advisory Board are called to a meeting by the Textile Commissioner several times a year in order to estimate the situation of the current cotton crops. Each member has a different estimation method. On the supply side, for example, state government officials visit the cotton fields of their state; the Ministry of Agriculture (central government) makes the statistical assessment on nationwide area and yield; the East India Cotton Association (EICA) observes how much cotton is ginned; and the CCI looks at the arrival of cotton in the market. On the demand side, each member estimates how much cotton will be consumed by domestic mills. Individual mills file statements every month to the Textile Commissioner reporting how much cotton they are using. Cotton consumption by small-scale mills is estimated based on the number of their spindles. Although the initial estimations by members differ from each other, consensus is made for one final estimate in the meeting. The Textile Comrnissioner reports the final estimate of surplus cotton to the Minister of Textiles. Based on his recommendation, the Minister of Textiles declares the nationwide quota level, which is divided into sub-quotas for different varieties of cotton.

Although the nationwide quota announcement is made once a year, the quota is allocated little by little to each trader several times a. year, depending on the fluctuating cotton market situation. In other words, the level of allocated export quota is subject to change throughout the year. Therefore, if the cotton scenario becomes tight at some stage during the year, it is possible that not all the export quotas for all the varieties of cotton announced in the beginning of the year are actually allocated by the end of that year. No 6
**Table 10: Robustness – Instrumental Variables Model: Firm Investment and Mobile Money Use**

||**Second-stage**<br>**regression**|**First-stage regression**|**Second-stage**<br>**regression**|**First-stage regression**|
|---|---|---|---|---|
|**Dependent variable:**|**Firm purchased**<br>**fixed assets Y:1**<br>**N:0**|**Firm used mobile**<br>**money for any**<br>**financial transactions**<br>**Y:1 N:0**|**Log of Purchase**<br>**of Fixed Assets**<br>**(amount in USD)**|**Firm used mobile money**<br>**for any financial**<br>**transactions Y:1 N:0**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Mobile money for any financial<br>transactions Y:1 N:0|1.237**||9.576**||
||(0.610)||(4.736)||
|Log of no. of Mobile Money Agents,<br>5 km radius||0.063**||0.063**|
|||(0.029)||(0.029)|
|Log of no. of MFIs, 5 km radius -<br>form||-0.045||-0.045|
|||(0.037)||(0.037)|
|Log of size|0.148***|-0.076***|1.718***|-0.076***|
||(0.053)|(0.027)|(0.418)|(0.027)|
|Top manager experience in sector<br>(years)|-0.006|0.004|-0.066*|0.004|
||(0.005)|(0.003)|(0.039)|(0.003)|
|Log of sales per worker (outliers<br>removed)|-0.018|0.007|-0.002|0.007|
||(0.021)|(0.016)|(0.172)|(0.016)|
|Log of age of firm|0.033|-0.062|0.227|-0.062|
||(0.062)|(0.039)|(0.509)|(0.039)|
|Firm is part of a larger firm Y:1 N:0|0.198|-0.214***|1.515|-0.214***|
||(0.161)|(0.070)|(1.315)|(0.070)|
|Exports 10% or more of sales Y:1<br>N:0|-0.181*|0.080|-1.643*|0.080|
||(0.108)|(0.081)|(0.925)|(0.081)|
|Foreign ownership Y:1 N:0|0.177|-0.074|1.219|-0.074|
||(0.149)|(0.093)|(1.301)|(0.093)|
|Firm with unfavorable credit record<br>Y:1 N:0|0.020|0.069|-0.413|0.069|
||(0.094)|(0.059)|(0.714)|(0.059)|
|Establishment has a line of credit or<br>loan at this time Y:1 N:0|0.068|0.002|1.584**|0.002|
||(0.087)|(0.062)|(0.721)|(0.062)|
|Firms use email to interact with<br>clients/suppliers Y:1 N:0|0.065|-0.014|0.918|-0.014|
||(0.095)|(0.067)|(0.743)|(0.067)|
|Establishment has checking or<br>savings account at this time Y:1 N:0|-0.013|-0.050|-0.269|-0.050|
||(0.112)|(0.098)|(0.885)|(0.098)|

34
# **5. How Do Banks Engage in Lending to SMEs?**

## **5.1 Business Model**

## **_5.1.1  Structure_**

Stand-alone SME units are increasingly being created by Colombian credit institutions, typically as part of the Retail Vice Presidency (VP). Almost half of all interviewed banks had fully operational and dedicated SME units to service this market segment, while the rest are moving in this direction but their operational structure is still incipient. For the former, SME units are typically part of the Retail VP since most client outreach and product delivery still takes place through bank branches (see below). SME units are usually involved in determining the product portfolio and in designing strategies to target new SMEs and cross-sell products to existing SME clients. Since banks perceive this segment as both costlier and riskier to operate, SME units also aim to improve operational efficiencies (e.g. by standardizing products and leveraging information technology) and reduce lending risks (e.g. by working with the risk management function to streamline the credit assessment process and to design credit ‘filters’ for RMs).

Branches continue to dominate as distribution channels, and very few banks have moved to a business center concept. All interviewed banks use branches as the most common distribution channel for the SME segment, while some banks offer electronic and phone banking as complementary means to reach clients (Figure 5.1). Some banks are moving towards hybrid branch models where clearly separated space, personnel, and back office operations are destined solely to service SMEs within a branch – an SME ‘branch within a branch’ model – while others service SMEs as part of a branch’s regular functions alongside retail clients. The concept of dedicated SME business centers is quite novel, and a few interviewed credit institutions have recently began to move in this direction.

Figure 5.1: Distribution Channels Used for SME Lending Figure 5.2: Role of Branches <!-- Start of picture text --> Branches 100%<br>Branches also perform some back-office 75%<br>functions<br>Electronic banking 63%<br>Phone banking 50% Branches are small banks within the bank<br>(front-office and back office tasks are 13%<br>Business Centers 13% processed locally)<br>Non-bank correspondents 13%<br>Branches are only delivery and client 13%<br>servicing channels<br>Mobile Units 0%<br><!-- End of picture text --> _Source: Authors’ analysis of bank questionnaires (simple averages across all banks). Note: Figure 5.1 does not add up to 100 percent because some credit institutions cited multiple distribution channels._ Headquarters are mostly involved in the later stages of granting a loan, while branches and sales personnel complement a number of centralized functions. Most credit institutions acknowledged that branches are not merely delivery and client servicing 28
of such proposals _ex ante_ thus may result in adoption of approaches that are not only unfair but also yield only a fraction of what may be possible. To illustrate the usefulness of low cost methods to obtain potential yield from a property tax but also to _ex ante_ assess revenue implications of alternative rate structures or exemptions, table 7 uses price predictions from the preferred (spatial error) model for exemption cut-offs set at 25% or 50% of mean property value.<sup>25</sup>

# **4. Conclusion and Policy Implications**

With current processes of urbanization differing from those observed historically in several respects, a city’s competitiveness will increasingly depend on its ability to address the ‘demons of density’ by planning ahead and raising the revenue needed to provide services effectively. The case of Kigali illustrates that high resolution remotely sensed imagery can be used to reliably check the completeness of valuation rolls and, if data on land values are available, also run land valuation models at a fraction of the time and resources required by more traditional technologies. This can enable cities in developing countries to augment the financial resources at their disposal, lessening dependence on central transfers; enhancing service quality; and possibly also helping to ensure currency of property registries to make private property rights more secure (Salm 2017).

To complement the results obtained here, three areas of enquiry seem promising. First, even if spatial data can be made available at low cost, least-cost (sampling) strategies to obtain the land values required to run a meaningful CAMA model are needed. Second, while several avenues to translate improved coverage with data on property tax into higher revenue exist—e.g., by reducing the cost of tax collection through mobile payments, applying peer pressure, etc.—knowledge on how to most effectively use these, or combine with adoption of differential rate structures, remains limited. Finally, the spatial data underpinning property tax rolls can also widen the range of options open to both the public and private sectors, from setting and enforcing building and planning standards to finding the most cost-effective ways to modernize infrastructure and other service provision and assessing the potential benefits from spatial data in this area would be of great interest for future research.

> 25 Similar results for the spatial autoregressive lag model are presented in appendix table 5.

14
# **Figure 6**

# **Real decoupling in EM: Decoupling with G7 and coupling with China**

This figure presents different betas from 2003 to 09/2010. Betas for G7 and China were estimated from dlog(EM) vs dlog(G7) dlog(China), were EM, G7, and China are real gdp growth or industrial production growth in EM countries, the G7 and China respectively. Betas were estimated by extending the time window. The first betas were estimated with the sample going from 1993 to 2003, and for the rest of the betas we extended the sample until 09/2010. Source: IMF IFS, GEM World Bank.

<!-- Start of picture text --> Real GDP Growth<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>G7 Beta G7 Beta +- 1 SD Linear (G7 Beta)<br>Industrial Production Growth<br>0.9<br>0.85<br>0.8<br>0.75<br>0.7<br>0.65<br>G7 Beta G7 Beta +- 1 SD Linear (G7 Beta)<br><!-- End of picture text --> <!-- Start of picture text --> Real GDP Growth<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>China Beta China Beta +- 1 SD Linear (China Beta)<br><!-- End of picture text --> <!-- Start of picture text --> Industrial Production Growth<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>China Beta China Beta +- 1 SD Linear (China Beta)<br><!-- End of picture text -->
# 1 Introduction

In recent years renewed efforts have been made (at the World Bank and elsewhere) to develop new policy tools aimed at better understanding the channels through which adjustment policies affect the poor and the possible trade-offs that poverty reduction strategies may entail regarding the sequencing of policy reforms. One approach consists in using disaggregated computable general equilibrium (CGE) models that integrate several representative household groups (RHG). These models try to capture a variety of channels through which stabilization policies and structural reforms affect growth, income inequality, and poverty.

A case in point is the Integrated Macroeconomic Model for Poverty Analysis (IMMPA) model developed by Agénor (2003), Agénor, Izquierdo and Fofack (2003), and Agénor, Fernandes and Haddad (2003). IMMPA models are typically based on a parsimonious five-good structure (rural, urban informal, urban private formal, urban public formal, and imported goods) and distinguish between a fairly small number of representative households. For instance, in Mini-IMMPA there are only five categories of households, consisting of workers in the rural sector, workers in the urban informal economy, urban unskilled workers in the formal sector, urban skilled workers in the formal sector, and capitalists-rentiers (see Agénor (2003)). Data from a household survey are classified into the categories of households contained in the structural component of the model. Following a policy or exogenous shock, real growth rates in per capita consumption and disposable income for all categories of households are obtained from the structural model, up to the end of the simulation horizon. These growth rates are applied separately to (disposable) income and consumption expenditure for each household in the survey, giving therefore a new vector of absolute income and consumption levels for each individual in each category or group of households. Poverty and income distribution indicators are then calculated with these new data, after updating the initial poverty lines (using the price indexes generated by the structural component of the model), to reflect changes in the price of the consumption basket and purchasing power of income. Because changes in within-group distribution are ignored, these indicators reflect essentially changes across groups.

While appealing from a practical point of view, this approach is open to the criticism that it does not account for heterogeneity among agents within groups and introduces only in a partial manner the relevant changes that 3
**Figure 1. Fiscal space indicators**

<!-- Start of picture text --> 80<br>GCC countries<br>70<br>60 Developing oil exporters<br>50 Oil importers<br>40<br>30<br>20<br>10<br>0<br>-10 2008 2010 2008 2011 2008 2011f 2008 2011f<br>-20<br>Government debt External debt (% Gross official Fiscal balance as %<br>as % of GDP of GDP) reserves, in of GDP<br>months of imports<br><!-- End of picture text --> Data sources: World Bank, IMF and Government sources.

Differences in the quality and quantity of infrastructure endowments and services across countries as well as differences in needs within countries and sectors make the identification of infrastructure needs quite complex in the region. Moreover, under a business as usual scenario, the gaps are likely to magnify as demand for infrastructure grows with population and income growth, and countries face challenges related to water and energy conservation, efficiency, and climate change.

When little detailed data are available to conduct a bottom-up approach, the assessment of infrastructure needs can be based on the estimation of econometric models of demand for each infrastructure subsector. Fay and Yepes (2003) and Yepes (2008) are among the best known papers illustrating the method across regions of the world. In this paper, we improve their earlier estimates for the MENA region by: (i) including data on infrastructure stocks for countries of the region from national sources as compared to relying on extrapolations from international databases; (ii) updating the data from original international sources; and (iii) including high income countries (GCC) in the definition of MENA. Demand for infrastructure is assessed by regressing per capita stocks of infrastructure against per capita income, the share of GDP derived from agriculture and manufactures, population growth and density, urbanization rate, and technology (Table 3).

4
# **Supplementary Materials**

# **Methods**

**<mark>Data Collection:</mark>** <mark>This paper leverages high-frequency phone surveys on COVID-19 that were implemented in Ethiopia, Malawi, Nigeria, and Uganda during the period of May-July 2020, with support from the World Bank Living Standards Measurement Study (LSMS) and the Poverty and Equity Global Practice. In each country, the phone survey aims to conduct monthly phone interviews, for a period of 12 months. The sample for these surveys is a national sample of households that had been interviewed during the latest round of the national longitudinal household survey implemented by the respective national statistical office, with financial and technical assistance from the World Bank LSMS-ISA initiative. Please see below for more information on the calculation of phone survey sampling weights.</mark>

<mark>The implementing agency for the phone survey in Ethiopia, Malawi, Nigeria, and Uganda, is, respectively, Laterite Ethiopia, Malawi National Statistical Office, Nigeria Bureau of Statistics, and Uganda Bureau of Statistics. The anonymized, unit-record phone survey data associated with each monthly survey round are made publicly available, within approximately four weeks of completion of phone interviews, through the World Bank Microdata Library, under the HighFrequency Phone Survey collection: http://bit.ly/microdata-hfps.</mark> <mark>The publicly-available data for each survey round is coupled with a basic information document, interview manual, and questionnaire for that round, which can be accessed through the following:</mark>

- <mark>Ethiopia: http://bit.ly/ethiopia-phonesurvey</mark>

- <mark>Malawi: http://bit.ly/malawi-phonesurvey</mark>

- <mark>Nigeria: http://bit.ly/nigeria-phonesurvey</mark>

- <mark>Uganda: http://bit.ly/uganda-phonesurvey</mark>

<mark>The approach to the phone survey questionnaire design and sampling is comparable across countries. It is informed by the template questionnaire and the phone survey sampling guidelines that have been made publicly available by the World Bank. These can be accessed through the following:</mark>

- <mark>Template Questionnaire: http://bit.ly/templateqx</mark>

- <mark>Manual: http://bit.ly/interviewermanual</mark>

- <mark>Sampling Guidelines: http://bit.ly/samplingguidelines.</mark>

<mark>All regressions and results presented in the paper are based on the publicly available data from the World Bank Microdata Library. Data is downloaded from the website and processed using Stata</mark> 15
<!-- Start of picture text --> 16.0<br>14.0<br>12.0<br>10.0<br>3.0 ===GDP Growth<br>(Including Oil)<br><!-- End of picture text --> <!-- Start of picture text --> 6.0 —— GDP Growth<br>4.0 (Excluding Oil)<br>2.0<br>0.0<br>ontooooBY CAIUS)ooooqgctodooes#oooncncoocnccocooccroeoe°oNn ASAIN,mMrtwHN OeStDOHOoHAnOt asCOSSB mMBt a+tOldNS+wi<br><!-- End of picture text -->
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Table 3: Reverse Cost-Benefit Analysis: Earnings Differentials Required to Realize 10 Percent Rate of Return

|||Varia|bles in Equ|ation (8)|||
|---|---|---|---|---|---|---|
||(Cs)|<br>(Ws)|<br>(ts)|<br>(Ds)|(Ws - Ws-,)|Observed as|
|Education level<br>(and investment<br>reference level)|Cost per<br>student<br>year<br>(VND'000)|Average<br>earnings<br>(VND'OOO|Assumed<br>cost<br>) periods<br>(years)|Required<br>differential<br>(VND'OOO)|Observed<br>differential<br>(VND'OOO)|percent of<br>required<br>(VND'000)|
|None<br>Primary||...<br>1,512||...|...||
|(vs none)|192|1,788|Cs: 5<br>Ws: 1|247|276|112%|
|Secondary|||||||
|(vs primary)<br>VOTECH|436|2,016|7|1,557|228|_15%_|
|(vs primary)<br>Tertiary<br>(vs secondary)|1,331<br>2,745|1,824<br>2,736|5<br>4|1,560<br>1,904|36<br>912|2%<br>48%|

Source: VLSS 1992-93.

The results of estimating a simple earnings function show education to be a marginal investment (Table 4). Overall, the average private rate of return on another year of schooling is 5 percent. This is very different from the previous and dated analysis limited to rural South Vietnam (Stroup and Hargrove 1969). An additional year of schooling at that time in South Vietnam led to an increase in earnings of 16.8 percent in 1964.

13
# Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS97|7 Income Security for Old Age:<br>Conceptual Background<br>and Major Is|Estelle James<br>sues|September<br>1992|D. Evans<br>37496|
|WPS97|8 How Restricting<br>Carbon Dioxide<br>and Methane Emissions<br>Would Affec<br>the Indian Economy|Charles R. Blitzer<br>t R. S. Eckaus<br>Supriya Lahiri<br>Alexander Meeraus|September<br>1992|WDR Office<br>31393|
|WPS97|9 Economic<br>Growth and the<br>Environment|Dennis Anderson|September<br>1992|WDR Office<br>31393|
|WPS98|0 The Environment:<br>A New Challenge<br>for GATT|Piritta Sorsa|September<br>1992|WDR Office<br>31393|
|WPS98|1 After Socialism and Dirigisme:<br>Which Way?|Andres Solimano|September<br>1992|S. Fallon<br>37947|
|WPS98|2 Microeconomics<br>of Transformation<br>in Poland: A Survey of State<br>Enterprise Responses|Brian Pinto<br>Marek Belka<br>Stefan Krajewski|September<br>1992|S. Husain<br>37139|
|WPS98<br>-<br>-|3 Legal Reform for Hungary's Private<br>.-Sa3tr.<br>.<br>.<br>-<br>.|Cheryl W.<sup>Gray</sup><br> Re.becca<br>J.JHanson.<br>Michael Heller|October 1992<br>**.**|R. Martin<br>39.065.|
|WPS984|Barriers to Portfolio Investments<br>in Emerging<br>Stock Markets|Asli DemirgOc-Kunt<br>Harry Huizinga|October 1992|W. Patrawimolpon<br>37664|
|WPS985|Regional Integration,<br>Old and<br>New|Jaime de Melo<br>Claudio Montenegro<br>Arvind Panagariya|October 1992|D. Ballantyne<br>37947|
|WPS986|The Administration<br>of Road User<br>Taxes in Developing<br>Countries|Roy Bahl|October 1992|J. Francis-O'Connor<br>35205|
|WPS987|How the Epidemiological<br>Transition<br>Affects Health Policy Issues in<br>Three Latin American<br>Countries|Jose Luis Bobadilla<br>Cristina de A. Possas|October 1992|0. Nadora<br>31091|
|WPS988|Economic<br>Valuation and the<br>Natural World|David Pearce|October 1992|WDR Office<br>31393|
|WPS989|The Indian Trade Regime|M. Ataman Aksoy|October 1992|R. Matenda<br>35055|
|WPS990|Protection and Industrial Structure<br>in India|M. Ataman<sup>Aksoy</sup><br>Francois M. Ettori|October 1992|R. Matenda<br>35055|
|WPS991|Environmental<br>Costs of Natural<br>Resource<br>Commodities:<br>Magnitude<br>and Incidence|Margaret E. Slade|October 1992|WDR Office<br>31393|