rafmacalaba/datause-encoder-data huggingface dataset:
# TABLE OF CONTENTS

|ABSTR|ACT......<br>..|i|
|---|---|---|
|ACKNO|ILEDGMENTS<br>.............|ii|
|TABLE|OF CONTENTS......................................................|iii|
|I.|INTRODUCTION<br>.................................................|......<br>1|
|II.|COMMODITYMODELING DEFINED .4||
|III.|RANGEOF MODELING METHODOLOGIES<br>.8<br>Market Models.|9|
||Spatial Equilibrium and Programing Models .12||
||Optimization<br>Models .17<br>System Dynamics Models .18<br>Input-Output<br>Models .19||
|IV.|COMMODITY MODEL APPLICATIONS<br>IN THE BANK .22<br>Early Model History<br>.22<br>Market Model Applications<br>.27<br>Programming<br>Model Applications<br>.37<br>Special Case of Energy ......................<br>................|. 40|
|V.|CONCLUDING PERSPECTIVE .......|44|
|REFER<br>BE R|ENCES.46<br>RNE<br>..............................................................|.4|
|FIGUR|E 1: MODEL REPRESENTATION<br>OFACOMMODITY MARKET .....<br>.........|......<br>5|
|TABLE|1: COMMODITY MODELING METHODOLOGIES<br>AND THE MODELING<br>PROCESS ........................................................|10|
|TABL|E 2:<br>CHARACTERISTICS OF WORLD BANKCOMMODITY MODELS ....<br>.......|.....<br>29|
Policy Research Working Paper 10894

# Can Public Credit Schemes Improve Access to Finance for Small Businesses?

## Evidence from Indonesia

_Salman Alibhai Hillary C. Johnson Cecile Thioro Niang Francesco Strobbe_ Finance, Competitiveness and Innovation Global Practice & East Asia and the Pacific Region September 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
(see McFadden, 1974), the probability that a type-h individual born in country o will move to country d is given by: and the log-ratio of emigrants to country d to non-migrants is determined by This model with a single skill dimension h is rich enough to depict some key patterns of international migration. Among these, the most important are the following:

- The size of bilateral migration flows Nod increases with the wage differential (wd − wo), differences in country fixed effects or amenities (Ad − Ao), and decreases with the level of overall migration costs (Cod).

- Migration rates are lower for low-skilled workers than for the high-skilled since the latter benefit more from wage differentials and have lower migration costs (positive selection).

- The proportion of high-skilled migrants is larger in countries with higher skill prices (positive sorting).

The key insight from the model is that it helps us to understand how existing diasporas affect the magnitude and structure of migration flows. In what follows, we denote the size of the diaspora from country o in country d by Mod. We show how diaspora externalities can be introduced into the equation (1) and how they can be empirically estimated using bilateral data on migration stocks and flows.

# 2.1 Impact on size

As mentioned above, existing social networks or diasporas reduce migration costs through two main channels. First, they lower information, assimilation and adaptation costs. For example, members of a diaspora can help new migrants find jobs, adjust to different social norms and navigate linguistic barriers. Second, family members who have migrated earlier and obtained certain legal rights in the destination country can lower the visa costs. This channel mainly operates by allowing migrants to benefit from family reunification programs for their legal entry into the destination country. For these reasons, the diaspora size are included in the determinants of migration costs and Cod becomes a function of Mod with ∂Cod/∂Mod < 0.

The size-externality of diasporas can be tested by regressing skill-specific bilateral flows, Nod(h), on the stock of existing migrants at the beginning of the period, Mod. Assuming a logarithmic functional form for the diaspora effect, equation (1) can now be rewritten as 4
price index calculated by Banco de Mexico. The discrimination between skilled and unskilled individuals is based on their level of education. Individuals with less than 9 years of education and without industry specific training are considered unskilled. Earnings have been converted in a hourly basis to make them comparable across individuals and household surveys. In-kind income and self-consumption were included in the measure of total households expenditures, income and welfare.

The matching between the finely disaggregated classification and the product aggregates utilized in this study has been done on the base of Ianchovicina, Nicita and Soloaga, 2002. Detailed concordance tables are available from the author on request.

The results of this paper are differentiated by state (entidad federativa). Figure A.1 illustrates the 32 states and their grouping in 5 regions.

# **A.2 Variable construction.**

# **A.2.1 Variable construction for the Pass-through price estimation.**

In estimating equation the _distance_ variable is given by the driving distance between the capital of each state and the United States border. The United States border is defined as the cities Tijuana, Ciudad Juarez and Nuevo Laredo.

_International prices_ indexes are calculated using commodity prices for agricultural products obtained by FAO and the World Bank. For manufacturing products, I utilize US price indexes by commodity published by the Bureau of Labor Statistics.

_Local supply_ is constructed as the production of that particular good that is produced by all the households in the state, in percentage term over all total agricultural production.

36
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local governments (Khemani, 2006a). In another country included in Table 1, the Philippines, although the average share of own-revenues is 14 percent, the share in the median municipality is 10 percent, and more than a quarter raise less than 5 percent of their total income from local sources.

Municipalities in Peru, the country with the highest share of own revenues among those included in Table 1, have been characterized by some analysts as having no discretion over local rate setting and tax collection, and effectively being fully financed by central grants (Ahmad and Garcia-Escribano, 2006).<sup>16</sup> Even this relatively high share of own revenues, at 27 percent, is comparable to the very lowest of such shares among OECD countries. Ambrosanio and Bordignon (2006) provide statistics on the share of grants in local government revenues in the 20 OECD countries, which shows that only 3 countries had a grants share greater than 55 percent—Ireland at 76 percent, and the Netherlands and UK at 70 percent. This OECD experience shows that even in the face of theoretical arguments in favor of grants financed decentralization, in which taxes can be collected more efficiently at national levels and then distributed to sub-nationals as grants, grants dependence is not in the realm of 80 or 90 percent of local revenues as in the countries listed in Table 1. Within the developing world, countries in the Latin American region appear to have a smaller share of municipal spending financed by grants, but even these are higher than in most OECD countries. In Brazil and Mexico grants constitute 67 and 64 percent of municipal revenues (Burki et al, 1999).

Grants dependence in these countries appears to go hand in hand with small size of local jurisdictions (in terms of population and area), and re-drawing of jurisdiction boundaries or growth in number of jurisdictions over time. Although the average population size of local jurisdictions is higher in Latin America than in Europe, this average appears to be masking large disparities across jurisdictions. Burki et al (1999) find that the vast majority of municipalities in the region have fewer than 15,000 residents. Burki et al (1999) also report shrinking in jurisdiction size, with the number of

> 16 Some have argued that Peru should not be considered a decentralized country at all. Indeed, prior to 2002 spending by municipalities hardly accounted for a significant share of government spending. However, since 2002, the share of municipal spending has risen to 13 percent and is expected to increase further. For the purposes of this paper, the issue of interest is that even as the country moves towards greater decentralization of spending, it chooses national grants to finance it.

19
**Table 5. Proportion of Men and Women Using Computers on the Job, by Country**

||Men|Women|Total|N|
|---|---|---|---|---|
|Armenia|0.41|0.48|0.43|530|
|Bolivia|0.43|0.35|0.39|653|
|Colombia|0.34|0.41|0.35|832|
|Georgia|0.41|0.55|0.44|481|
|Kenya|0.28|0.22|0.24|1160|
|Ukraine|0.41|0.45|0.42|730|
|Vietnam|0.37|0.35|0.38|1394|

_Note_ . The sample includes 25- to 64-year-olds in self-employment or wage work. _Source_ : STEP Surveys (2014).

Looking at the distribution of men and women by the complexity of computer-related tasks that they engage in on the job (see Table 6), we find that in Armenia, Colombia, Georgia, Ukraine, and Vietnam, the largest proportions belong to the group using MS Office applications. The associated tasks are word processing, making presentations, and using basic graphic applications. In Bolivia and Kenya, the largest proportion of men and women are found using advanced programming tasks on the job (22 percent of men and 16 percent of women in Bolivia, and 14 percent of men and 8 percent of women in Kenya).

**Table 6. Proportion of Men and Women by Complexity of Computer Use, Both on the Job and by Country**

||Bro<br>based|wser-<br>tasks|Basi<br>Office|c MS<br>tasks|Ba<br>progra|sic<br>mming|Adva<br>progra|nced<br>mming|
|---|---|---|---|---|---|---|---|---|
||M|W|M|W|M|W|M|W|
|Armenia|0.06|0.02|0.20|0.28|0.09|0.08|0.07|0.09|
|Bolivia|0.02|0.01|0.14|0.12|0.06|0.06|0.22|0.16|
|Colombia|0.01|0.01|0.15|0.18|0.08|0.11|0.10|0.11|
|Georgia|0.04|0.04|0.15|0.27|0.10|0.13|0.11|0.11|
|Kenya|0.01|0.01|0.05|0.06|0.08|0.06|0.14|0.08|
|Ukraine|0.04|0.03|0.16|0.20|0.15|0.17|0.07|0.05|
|Vietnam|0.04|0.01|0.15|0.18|0.06|0.05|0.13|0.10|

_Note_ . The sample includes 25- to 64-year-olds, in wage work or self-employment, using computers on the job. Highlighted cells indicate statistically significant differences between men and women. _Source_ : STEP Surveys (2014).

With regard to noncognitive skills, we find limited variation among men and women across average scores on the Big Five personality traits, grit, and decision-making. The average scores and standard deviations are presented in Table 7. While there are some differences in average 17
**Figure 2.8 Morocco: Evolution of investment efficiency, 2001‐17**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>y = 0.6187t + 3.2377<br>15<br>10<br>5<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>ICOR Linear (ICOR)<br><!-- End of picture text --> _Source_ : High Commission of Planning (HCP).

The diminishing efficiency of public investment, as well as possible crowding‐out effects (given a limited pool of national savings) and a limited complementarity effect associated with public capital, may help to explain why private sector investment in Morocco has remained at a relatively low level of around 16 percent of GDP (just over half of total investment) over the past decade.<sup>9</sup>

# **2.3.2 Persistent Unemployment, Inadequate Labor Quality**

# _Persistent Unemployment_

Despite Morocco’s favorable growth performance recorded over the past decades, the unemployment rate has remained stubbornly high, at about 10 percent in recent years, particularly for young and skilled workers. Youth unemployment exceeds 20 percent and the unemployment rate for graduates is 15 percent. Two out of three unemployed people are youth aged 15 to 24 years (with little or no education) and one of four is a higher education graduate (Figure 2.9). Almost 28 percent of the youth is neither employed nor in education or training. In addition, four of five unemployed are urban residents, one of two unemployed people is a first‐time job seeker, and nearly two out of three unemployed people have been unemployed for over a year. Thus, unemployment in Morocco is largely structural. Long‐term unemployment discourages job search and accelerates the loss of skills, making it more difficult to enter, or return to, the workforce. This is particularly worrying in a country where more than half the population is below 30 years of age. Yet, unemployment is a regional issue. According to the MENA Economic Monitor, more than 11 percent of the region’s active population is unemployed, with more than 30 percent of youth. Unemployment of the region’s educated youth is particularly worrying and questioned the concept of a “skills premium”

> 9 As discussed later, another constraining factor relates to the fact that the business environment, despite notable improvements in recent years, is still in need of progress.

11
<!-- Start of picture text --> UAE: Spactral Density Function of Real Credit Algeria: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>Qatar: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>B- Oil importers<br>Jordon: Spactral Density Function of Real Credit Morocco: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram -2 log Periodogram -2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram-2 log Periodogram-2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br><!-- End of picture text --> 27
Nugent, J. B. and J. A. Robinson. 2010. "Are Factor Endowments Fate?" _Revista de Historia Economica_ , _28_ (1), 45-82.

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Rajan, R. and R. Ramcharan. 2011. "Land and Credit: A Study of the Political Economy of Banking in the United States in the Early 20th Century." _The journal of finance_ , _66_ (6), 1895-1931.

Ricker-Gilbert, J., C. Jumbe and J. Chamberlin. 2014. "How Does Population Density Influence Agricultural Intensification and Productivity? Evidence from Malawi." _Food Policy_ , _48_ , 114-128.

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Saidi, P. M. 1999. "Final Report of the Presidential Commission of Inquiry on Land Policy Reform.", Government of Malawi, Lilongwe.

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33
<!-- Start of picture text --> Occupational Status Scores: NZSEI<br>55<br>50 -<br><!-- End of picture text --> <!-- Start of picture text --> 45 —<br>40 -<br><!-- End of picture text --> <!-- Start of picture text --> 30 |<br>Pre-migration 1stNZjob Jobat1st Jobat2nd Priorjob* Jobat 3rd<br>(retrosp) (retrosp) survey survey survey*<br>*measured only for those changing<br>main occupation in last 5 years<br><!-- End of picture text -->
Figure 2: DALY's From Outdoor Air Pollution <!-- Start of picture text --> 4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>Ar Dalys<br>0- 2480<br>2506 -26011<br>27732 - 137105<br>140164<br>154843-2111123<br>*- nla.<br><!-- End of picture text -->

> Source: Pandey, et al. (2003)

33
Table 5: Effect of PVP on Length of Women’s Speech

|||||_Depen_|_dent variable_|_:_|||
|---|---|---|---|---|---|---|---|---|
||Sp|eech Length|(All Speaker|s)||Speech Leng|th (Citizens O|nly)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I(Female)|_−_16.97<sup>_∗_</sup>|_−_13.38<sup>_∗_</sup>|_−_16.95<sup>_∗_</sup>|_−_17.32<sup>_∗_</sup>|_−_4.33|_−_3.72|_−_7.48<sup>_∗∗_</sup>|_−_4.54<sup>_∗_</sup>|
||(8.83)|(7.74)|(9.13)|(8.86)|(2.68)|(2.84)|(3.49)|(2.55)|
|I(PVP)|_−_10.02|_−_9.33|_−_7.63|_−_8.73|_−_5.46|_−_4.88|_−_5.74|_−_5.04|
||(9.96)|(7.98)|(8.75)|(9.07)|(3.77)|(3.33)|(4.11)|(3.70)|
|I(Female) x I(PVP)|21.91<sup>_∗_</sup>|19.02|23.82<sup>_∗_</sup>|22.43<sup>_∗_</sup>|51.92<sup>_∗∗∗_</sup>|51.44<sup>_∗∗∗_</sup>|56.04<sup>_∗∗∗_</sup>|52.13<sup>_∗∗∗_</sup>|
||(12.68)|(12.13)|(13.54)|(12.97)|(9.36)|(8.93)|(10.25)|(9.49)|
|Matched Pair FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Demographic Controls||✓|✓|||✓|✓||
|Infrastructure Controls|||✓||||✓||
|_p_-Score Control||||✓||||✓|
|Observations|3,894|3,894|3,894|3,894|2,130|2,130|2,130|2,130|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages. Demographic controls include: number of households, percentage Scheduled Caste, percentage Scheduled Tribe. Infrastructure controls include indicators for the presence of a primary school, secondary school, health center, hospital, clinic, medicla shop, government hospital, and bank.

Tird, we examine the effect of PVP on the _length_ of floor time enjoyed by women (Table 5). Here, we proxy for floor time using the word count of each speech. Consistent with previous work, we find that women generally occupy less floor time then men, about 16 fewer words per speech (compared to an average of 78 words per speech for men in control villages); given that fewer women speak overall, this leads to a massive disparity in floor time. PVP, however, has a substantial impact on women’s length of speaking, increasing the average speech length by over 20 words for the full sample, and by over 50 words per speech among citizen speeches. Tis disparity not only closes the gender gap in floor time, but actually enables women to take up a majority of the conversation.

# _4.2 Deliberative Influence_

While women are speaking significantly more in our treatment villages, their voices may still go ignored. Previous empirical work has shown that women are significantly less likely than men to drive conversation or set the agenda (Karpowitz and Mendelberg, 2014; Parthasarathy et al., 2017). To examine whether PVP improves women’s ability to influence discussion, Table 6 regresses two measures of agenda-seting power — the likelihood that the following speech is on 20
provided by a subsidiary located in the borrower country, while Third-country subsidiary/total volume is the share of loans provided by a subsidiary located in a third country. These two variables have means of 0.018 and 0.077, respectively; implying that loan provision through third-country subsidiaries is relatively common.

Our main independent variables are indices of the quality of bank regulation taken from the World Bank’s Bank Regulation and Supervision Surveys (Barth et al., 2004 and 2006). The available information is from five consecutive surveys. Following Houston, Lin and Ma (2012), we take values of the regulatory variables for the years 1995 to 1999 from the first survey (measuring regulation in 1999); for the years 2000 to 2003, we use the second survey reflecting the quality of regulation as of the end of 2002; for the years 2004 to 2007, the third survey’s results are used (reflecting regulation at the end of 2005); for the years 2008 to 2012, we take the results of the fourth survey documenting regulation at the end of 2012; finally, for the years 2013 to 2016 we use the results of the fifth survey measuring regulation at the end of 2016.<sup>9</sup> Among the regulatory variables, Capital regulation (borrower) is an index of the stringency of capital adequacy standards in the borrower country, with higher values indicating greater stringency. Capital regulation (borrower) ranges between 1 and 10 and has a mean of 6.333. Overall activity restrictions (borrower) is an index of the extent to which banks can engage in securities, insurance and real estate activities in the borrower country, with higher values indicating more restrictions. This variable ranges between 3 and 12 and has a mean of 7.121. Official supervisory power (borrower) is an index of the power of the supervisory authorities in the borrower country to take specific actions to prevent and correct

> 9 <mark>One of the subcomponents of Capital regulation (Overall capital stringency) cannot be calculated using data from the fifth wave of the survey because of missing information. Hence, for this wave we take the values of Capital regulation in the fourth wave and adjust it using changes in the other subcomponent, Initial capital regulation, that is available.</mark>

9