rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1: Outstanding Bond Issues by Credit Rating, October 2003**

<!-- Start of picture text --> 1600 100<br>90<br>1400<br>80<br>1200<br>70<br>1000<br>60<br>800 50<br>40<br>600<br>30<br>400<br>20<br>200<br>10<br>0 0<br>Debt Outstanding (bln pesos) Number of issues<br>AAA AA+ AA AA- A+ A A- BBB+ BBB BB+ BB B C<br><!-- End of picture text -->

**Source:  SVS Chile**

This clustering of credit quality is attributable to the nature of the investor base in Chile. The pension reform in 1982 created a mandatory contributory system with pensions managed by AFPs (Administradores de Fondos de Pension), and a payout phase for retirement which is offered either via a particular product by the AFPs or through annuities offered by insurance companies. As a consequence, assets managed by these two institutional investors have grown dramatically in the past two decades. Total AFP assets outnumber those of the insurance companies by a factor of nearly three (US$35 billion vs US$12bn), though insurance companies have been more actively investing in corporate debt in the past few years. However, from 2000 to 2001, insurance companies nearly doubled their exposure to corporate debt, both in total amount held and as a share of their portfolio. At the end of 2002, insurance companies held 24.2% of their portfolio in debentures, at just over US$3 billion. AFPs have held a relatively constant share of their portfolio in corporate debt since 1995, climbing to just over 7% (roughly US$3 bn) at end 2003.

These two institutional investors combined dominate the demand side for corporate debt, as all other investors comprise only trivial holdings in comparison. As a result, companies in Chile are heavily swayed by the wishes of this concentrated investor base; 2 of the 7 AFPs and the largest 5 of the 17 insurance companies that sell annuities hold the majority of the corporate investments. There is very little deviation between portfolios across AFPs.<sup>3</sup> The dominance of these firms has shaped the nature of both the primary and secondary markets. First, the liability duration of AFPs is roughly 8 years, and that of insurance companies is about 18-20 years, so that any corporate wishing to place large amounts of paper typically has to issue two tranches in these maturities. Second, due to the investment regulations in these institutions, only highly rated issues

> 3 In mid-2002 AFPs began to offer five different funds (Type A through Type E) with different risk levels and investment limits.  However, across AFPs, the portfolio composition of each fund type remains very similar.

8
||Plan|1|Plan|2|Plan|3||||
|---|---|---|---|---|---|---|---|---|---|
||1Mediumda<br>Someirrigatio<br>Noforestmgm<br>Performance|m<br>n canals__<br>t<br>(Regret)|2Small Dams<br>| Large irrigati<br>Large forest m<br>|Performance|on ponds<br>gmt<br>(Regret)|1Small Dam<br>Someirrigati<br>Small forestm<br>|Performance|on canals<br>gmt<br>(Regret)|Best<br>(Performance<br>In Scenario|Planw/Best<br>Performance<br>in Scenario||
|ScenarioA<br>||||||||||
|SameRainfall|4|oO|3|i|3|i|4|Plan 1||
|SameTimber||||||||||
|Scenario B<br>More Rainfall|-2|5|3|oO|2|i|3|Plan 2||
|MoreTimber||||||||||
|ScenarioC||||||||||
|LessTimber||||||||||
|ScenarioD<br>LessRainfall|1|1|1|1|2|fy)|2|Plan 3||
|LessTimber||||||||||
|Best<br>Performance<br>of Plan|4||||||4|Plan1|Planwithhighestperformancein<br>anysinglescenario|
|Performance<br>of Plan|0.75||||||25|Plan3|Plan with highestaverage<br>“<br>ae<br>performanceacross allscenarios|
|Maximum<br>Regret <sup>of Plan</sup>||5||1||1|1|Plan2Plan3|Plan withlowestmaximumregret<br>.<br>across allscenarios|
|Average Regret<br>gems<br>ofPlan||2.25||0.75||05|0.5|Plan3|Plan with lowest average<br>regret<br>es<br>acrossallscenarios|
Table A2—Probit regression results of selection equation of wage (dependent variable: wage being observed)

||F<br>|ull Sample||Trim<br>|med Sampl|e|
|---|---|---|---|---|---|---|
||Coeff.|t||Coeff.|t||
|Years of schooling|-0.065|-2.63|***|-0.056|-2.2|**|
|Age|0.969|4.03|***|0.963|3.87|***|
|Age squared|-0.023|-3.52|***|-0.023|-3.38|***|
|TEEP cohort*TEEP province|0.359|0.86||0.108|0.25||
|Years of schooling*TEEP cohort*TEEP province|-0.033|-0.71||-0.009|-0.19||
|TEEP cohort|0.291|0.64||0.566|1.2||
|Years of schooling*TEEP cohort|-0.169|-4.07|***|-0.197|-4.61|***|
|Female*TEEP cohort*TEEP province|-0.245|-1.81|*|-0.251|-1.79|*|
|Female|-0.292|-0.86||-0.237|-0.68||
|Female*Years of schooling|0.021|0.7||0.018|0.59||
|Female*TEEP cohort|0.578|3.91|***|0.566|3.72|***|
|Mother’s years of schooling|-0.044|-4.9|***|-0.039|-4.22|***|
|Mother’s age|-0.006|-1.69|*|-0.005|-1.55||
|Log(consumer durables)|-0.161|-7.84|***|-0.175|-8.23|***|
|Height|0.005|1.77|*|0.005|1.73|*|
|Number of students per classroom in high school|0.002|1.48||0.001|1.24||
|Constant|-7.100|-3.19|***|-6.988|-3.02|***|
|Number of observations|2,962|||2,761|||
|Pseudo R-squared|0.237|||0.238|||

Source: TEEP Tracking Survey. Notes: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

43
significant impact in increasing enrollment, except when using the measure asking parents to show evidence of enrollment (p-value: 0.135).<sup>16</sup> In the comparison between conditional and unconditional transfers (Panel B), the coefficient on “treatment” can be interpreted as the impact of conditional cash transfers. Again, these coefficients are all positive and significant, indicating that conditional cash transfers encouraged school enrollment. The coefficient on the UCT indicator is negative for the 3 measures and significantly so for the first two, indicating than unconditional cash transfers were less effective than conditional transfers in increasing enrollment. This finding is confirmed when looking at the p-values reported got “Treatment + UCT”: only for the school-based measure does the p-value suggest a combined coefficient statistically different than zero.

The impact of the cash transfers on enrollment at round 2, one year into the experiment, is not as strong or significant. Only using the school-based measure (column 1), can we detect a significant impact of cash transfers (transfers given to fathers and both conditional and unconditional cash transfers). One likely explanation of this more limited impact of cash transfers at round 2 is that due to logistical reasons, the cash transfer program was unexpectedly launched late by Burkina Faso’s government in the 2008-2009 school year. The first cash payment was only made at the end of November/early December 2008, while the school year started October 1, 2008. This meant households did not receive the transfer in time to pay school fees due at the start of the academic year and this might have reduced their ability to enroll their children during the program’s first year.

Column 4 reports the cash transfers’ impact on unconditional school attendance. Given that attendance conditional on enrolment is very high (98.3%) since once enrolled children tend to attend classes, our approach consists of estimating the impact on average school attendance rates of all school age children, unconditional on their enrollment. This is a broad measure of school participation with direct policy relevance that accounts for the enrollment and attendance effects and is not confounded by changes in the share of the sample enrolled. We rely on a measure taken from school ledgers collected at the school level<sup>17</sup> : for each child, we compute the

> 16 A recent cash transfer program aimed at improving secondary school enrollment in Macedonia randomly gave conditional cash transfers to mothers or fathers, and evaluation results also found that specifically targeting mothers did not significantly improve school enrollment (Armand 2014).

> 17 We also collected parental self-reports for attendance but the recall period was the last two weeks and since some villages were surveyed after the end of the school year, we consider this measure to be less reliable.

13
24 Revenue Adequacy and Growtht. In the previous section data regarding the relative size and growth rates in revenues from grant programs were shown. Whether or not the allocations are, somehow, adequate is, of course, nearly impossible to ascertain. One approach would be to allow recipient governments to put forward requests for funds and observe if these requests are granted; however, the resulting allocations will likely depend more on the ability of the local government to present its argument and on its political clout at the granting government level than on any real level of need. Another approach is determination of needs 18 based upon objective criteria. While such allocation mechanisms may be based on a complex basis using a large set of criteria, data restrictions in most developing countries generally limit the use of this approach.

It is, however, common to base allocations at least partially upon "need" when it is approximated by the size of the population residing in the regional government or by one or two additional factors such as miles<sup>of</sup> This approach to developing grant allocations is discussed in R.J. Bennett, Central Grants to Local Governments (Cambridge: Cambridge University Press, 1982), Chapter 9; Richard Jackman, "Estimating Expenditure Need: The Use of Regression Analysis in England and Wales," in Measuring Local Government Expenditure Needs, ed. Cameron and Lotz, pp. 2138 and Joegen Lotz, "Social Needs Equalization: The Distribution of General Grants to Local Governments in Denmark," in Measuring Local Government Expenditure Needs, pp. 39-56.

> 1 9 0ne attempt at designing a "needs" based allocation mechanism was carried out by West Bengal Municipal Finance Commission. For a discussion of the recommendations of that Commission see, Abhijit Datta, StateMunicipal Fiscal Relations: A Comparative Study of Australia and India, Research Monograph No. 37 (Canberra: Centre for Research on Federal Financial Relations, The Australian National University, 1982).
propensity to innovate (see equation 1 below). In ancillary regressions not reported in the paper, we compute the innovation score by considering just enterprises that provided answers for all the questions (i.e., unweighted average). Results are insensitive to this change.

We distinguish between adoption (product and process) and invention (proxied by undertaking R&D expenditures) to capture the extensive margin. For adoption, we can construct measures related to the extent of firm innovation and the decision to innovate. The _adoption score_ is quantified similarly to the innovation score, but it excludes expenditures in R&D. The focus is on existing processes, technologies, products, and services that are new to the domestic market where a firm operates. We also construct an _adoption dummy_ that takes a value of 1 if the adoption score is greater than 0. This variable is used in logistic regressions that estimate the relationship between a firm financial structure and the decision of adoption. Finally, we construct a variable ( _Invention_ ) to measure a firm decision to innovate, proxied by the undertaking of R&D expenditures. The variable takes a value of 1 if a firm has recorded expenses in research and development, 0 otherwise. Table 1 below reports the variable names and description of the firms’ innovation variables employed in the empirical approach.

# _2.3 Financial structure and sources of funding_

Our main variable of interest is the financial structure of firms, measured using three separate variables that quantify the proportion of fixed assets financed through different sources. The first ratio quantifies the proportion of fixed assets financed by banks. As shown in Table 2, on average bank funding is the most important source of external funding. The second variable is the percentage of fixed assets financed through non-bank financial institutions. The third variable is the percentage of fixed assets financed through other sources, including credit/advances from suppliers/customers.

# _2.4 Other variables_

We account also for additional firm characteristics that may affect the decision of and the extent to which a firm innovates. _Size_ is measured through the natural logarithm of the number of employees. In principle, larger firms are better equipped to innovate more because of larger cash flows, lower fixed costs, and a wider range of human capital skills and knowledge. Yet small size allows for more flexibility in the undertaking of innovative projects (Rogers 2004). We also include a labor productivity variable as firms with a skilled workforce are more likely to pursue innovative projects. The literature documents that innovation of all types tend to have a positive impact on firm-level productivity, as per the surveys of 7
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<!-- Start of picture text --> @ Marketincome © Disposable income<br><!-- End of picture text -->
- Muralidharan, K., P. Niehaus, and S. Sukhtankar. forthcoming. “Building state capacity: Evidence from biometric smartcards in India.” _American Economic Review_ , pp. .

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- Van Den Berg, M. 2002. “Do public works decrease farmers’ soil degradation? Labour income and the use of fertilisers in India’s semi-arid tropics.” _Environment and Development Economics_ 7:487–506.

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- Kilic, T., and Moylan, H. (2016). “Methodological experiment on measuring asset ownership from a gender perspective (MEXA) technical report.” Washington, DC: World Bank. Retrieved on May 8, 2017 from <u>http://tinyurl.com/mexareport.</u>

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- Meinzen-Dick, R., Johnson, N., Quisumbing, A., Njuki, J., Behrman, J., Rubin, D., Peterman, A., and Waithanji, E. (2011). “Gender, assets, and agricultural development programs: a conceptual framework.” IFPRI CAPRi Working Paper No. 99.

- Menon, N., Rodgers, Y., and Nguyen, H., (2014). “Women's land rights and children's human capital in Vietnam.” _World Development_ , 54, pp. 18–31.

- Palacios-Lopez, A., Christiansen, L., and Kilic, T. (2017). “How much of the labor in African agriculture is provided by women?” _Food Policy_ , 67, pp. 52-63.

- Quisumbing, A., Payongayong, E., Aidoo, J.B., and Otsuka, K. (2001). “Women's land rights in the transition to individualized ownership: implications for the management of tree resources in western Ghana.” _Economic Development and Cultural Change_ , 50.1, pp. 157–182.

- Quisumbing, A., and Maluccio, J. (2003). “Resources at marriage and intrahousehold allocation: evidence from Bangladesh, Ethiopia, Indonesia, and South Africa.” _Oxford Bulletin of Economics and Statistics_ , 65.3, pp. 283–327.

- Quisumbing, A., Kumar, N., and Behrman, J. (2011). “Do shocks affect men’s and women’s assets differently? a review of literature and new evidence from Bangladesh and Uganda.” IFPRI Discussion Paper  No. 1113.

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31
**Figure 4. Total LVAX share by income group**

<!-- Start of picture text --> 0.55<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>1995 1997 2001 2004 2007 2011<br>High, whole sample High<br>Middle, whole sample Middle<br><!-- End of picture text --> Note: Income group is held constant for the time series and is defined according to the World Bank country group 1995 classification. In the subsample of countries present in the database since 1995, low-income countries are represented by China, India, Sri Lanka and Vietnam; middle-income countries by Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay and República Bolivariana de Venezuela; high-income countries by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA. Income group for the point comparison is defined according to the World Bank country group 2011 classification.

Figure 5 shows the breakdown of the total labor value added share in exports for each income group in 2011. It illustrates differences in the importance of domestic sourcing as opposed to the direct labor value added in export industries, as well as differences in the skill composition of each component.<sup>14</sup> The group of high-income countries has the highest share of labor value added in exports, driven entirely by a relatively large share of direct high-skilled labor value added; this component is smaller by half in the middle- and low-income groups, which on the other hand have the highest component of unskilled labor directly contained in exports. This relationship is a direct implication of the Heckscher-Ohlin theorem, whereby a country will export goods that use its abundant factors intensively. This is also consistent with other literature that finds skills are a necessary factor for countries to increase domestic value addition in their exports (Miroudot and Squicciarini, 2015 and Farole, 2016).

On the other hand that does not apply to the indirect labor content of exports, where the skill composition is similar across income groups, with the skilled labor share around 7% and the unskilled labor share around 11% of gross exports. This suggests that sectors providing domestic inputs to exports are less subject to the comparative advantage rule than the export sectors themselves. Thus skilled labor in developing countries appears to be relatively more important for the inputs to the exports than for the exports themselves.

> 14 The blue parts of the bars represent the direct content and the green parts the indirect content of wages (i.e. the domestic wages paid to those sectors providing inputs for the exports). The darker parts of the bars represent wages paid to skilled workers while the lighter parts represent unskilled workers.

12
variables to proxy four of these channels, but the district data on land and consumer prices are not available separately for rural and urban areas. We add these variables to both the baseline (as defined in (2)) and the IV specifications to obtain a sense of the relative importance of these channels.

To capture the consumption linkage effect of urbanization, we use cropspecific shares of cultivated land. We interact this share at the beginning of the period of our analysis with the urban population variable to test the impact of urbanization across districts on the basis of their production dependence on specific crops. This measure relies on the assumption that a district’s supply is a good proxy for urban demand. To identify the relevant crops, we first test for the relationship between urbanization and the main crops across Indian districts.<sup>29</sup> Among the crops that display a significant relationship, we then select the relatively large crops, those with a share of total cultivated land above 5 percent throughout the period. These cultivations are most likely to affect rural economic livelihood.

These crops are rice (which covers, on average, 15 percent of the total cultivated land) and pulses (8 percent). These are important expenditure items for Indian households (Subramanian and Deaton, 1996). India is the world’s largest consumer and producer of pulses and is one of the largest consumers and producers of rice. This choice is reinforced by the results of Jha et al. (2005), who show that the rice market is not integrated within India. As shown below, our analysis suggests that urbanization is associated with an increase in the cultivation of rice and a decrease in the cultivation of pulses. Although the latter result is expected (because pulses are considered a relatively poor crop), the former result is surprising because rice is usually substituted with wheat and other food products as income rises. One reason that this substitution might not occur in India is that during the period of our analysis, 30
Policy Research Working Paper 9362

# **Abstract**

This paper evaluates the impact of two export finance support schemes, the Export Finance Scheme and the Long-Term Finance Facility for Plant & Machinery on firm-level export performance in Pakistan. These policies offer loans to exporters at concessionary interest rates to finance short-term working capital and long-term investment in machinery and equipment, respectively. The paper combines customs data with information on firms that participated in each scheme and the value of the loans they obtained between 2015 and 2017. Using matching estimators to control for the nonrandom selection of firms into the schemes, the analysis finds that the Export Finance Scheme and Long-Term Finance Facility for Plant & Machinery increased the growth rate of export sales by 7 and 8‒11 percentage points, but they do not have a significant impact on the number of products that a firm exports or the number of foreign countries to which it sells to. A cost-benefit analysis shows that although both schemes deliver net benefits, they entail a substantial financial cost to Pakistan’s central bank.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 gvarela@worldbank.org.

_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