rafmacalaba/datause-encoder-data huggingface dataset:
which does not hold in low-income countries. The link between exports and demand for skills is also different across low and middle-income countries. Among low-income countries there is no systematic correlation while among low-middle and middle-income countries the correlation is negative and strong. A closer investigation shows that this finding in the low-middle-income countries is driven only by China, where the specialization effect of exports dominates (see also Fajnzylber and Fernandes, 2004).

Finally, we investigate the robustness of our main findings to the skills measure used in the paper (captured by share of workers with more than secondary education). Alternatively, we measure skills using the share of managers, professionals and nonproduction workers in the total workforce. The results in table 11 show that qualitatively the results are similar to the ones reported in table 8 although the effect of FDI on skills is not statistically strong. This suggests that although the occupational classification is similar, firms with foreign capital have a more educated workforce. These findings reinforce the fact that the two skills measures do not capture the same dimension of the human capital in the firm.

# **6. Conclusion**

East Asia is undergoing a deep structural change with the demand for skills increasing significantly in the region, even within narrowly defined sectors. Job creation in the region is also increasingly biased towards the use of more skills. This paper investigates the extent to which the increased openness (captured by exports and foreign direct investment) and recent technological innovation in the region have contributed to a greater demand for skills and, thus, for the observed skill upgrading. We explore a large cross-sectional micro data across eight countries in East Asia.

Our results are consistent with greater openness and technological innovation at the firm level being associated with an increasing demand for skills, especially for middle-income countries. In particular, we show that the presence in international markets through exports has been skill enhancing for most middle-income countries, although this is not the case for firms operating in China and in low-income countries. There, a stronger integration in international markets is associated with a greater specialization in relatively low skilled intensive goods. These findings strongly suggest 19
Figure 8. Reference case forecasts of power generation <u>(LHS) and CO2 emissions from power generation (RHS)</u> <!-- Start of picture text --> 120 Hydro 60<br>Import<br>100 Gas 50<br>Coal<br>Oil<br>80 CO2 Emitted 40<br>60 30<br>40 20<br>20 10<br>0 0<br>2015 2017 2019 2021 2023 2025<br>TWh tCO2<br><!-- End of picture text --> In the Reference Case, gas will continue to dominate the generation mix until 2023 when imports of power and imported coal will become the major source of power. Gas generation will drop from 75% of total generation in 2015 to 51% by 2025. Imported coal based power will account for 31% of generation and the share of imports will rise from 9% to 17% of generation. Due to the projected increase in coal based generation, CO2 emissions will rise from 24 mt CO2 in 2015 to 65 MTCO2 by 2025.

The reference case scenario reflects the fact that coal generation remains the cheapest source of power even as Bangladesh will need to import almost of its additional coal demand. The reference case assumption implies that levelized generation costs will rise from 3.1USc/kWh to 7.7USc/kWh by 2025. While this is a steep increase in costs, the current system costs observed in Bangladesh is actually even higher at 7 Tk/kWh than the 2025 estimate. The reason for the difference between the estimate and observed costs has to do with the absence of merit order dispatch in Bangladesh. Least‐cost optimization maximizes generation at the lowest cost. This inherently assumes merit order dispatch – an issue that has been analyzed by Nikolakakis et al (2016).

Figure 9. Hourly dispatch during peak demand in 2015 Figure 10. Hourly dispatch during peak demand in 2025 (MW) <!-- Start of picture text --> 20 Fuel oil LNG<br>Domestic gas Hydro<br>18 Import Import coal<br>16 Domestic coal<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 3 5 7 9 11 13 15 17 19 21 23<br>MW<br><!-- End of picture text --> <!-- Start of picture text --> 9 Fuel oil Domestic gas<br>8 Hydro Import<br>Domestic coal<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1 3 5 7 9 11 13 15 17 19 21 23<br>MW<br><!-- End of picture text --> 16
allowing for heterogeneity can lead to very different estimates of a program’s effect. The proposed method offers a simple way of dealing with such heterogeneity.

The approach has three advantages. First, by using observational data for a random sample from the population of intended beneficiaries external validity is ensured. While the disadvantages of observational data are well known, this is an important advantage. Secondly, by focusing on the combined effect of program components they are automatically correctly weighted. Finally, it avoids the problems which RCTs encounter when assignment is imperfectly controlled and correlated with unobservables, as is plausible in development programs.

17
# **Contents**

|I .<br>I ntr|oduction ........................................................................................................................1|
|---|---|
|I I .<br>Defi|ning “Must Have” Features for an E fficient Public Investment System ...............3|
|1.|Investment Guidance, Project Development & Preliminary Screening .....................3|
|2.|Formal Project Appraisal ...........................................................................................3|
|3.|Independent Review of Appraisal ..............................................................................7|
|4.|Project Selection and Budgeting ................................................................................7|
|5.|Project Implementation ..............................................................................................8|
|6.|Project Adjustment .....................................................................................................9|
|7.|Facility Operation ......................................................................................................9|
|8.|Basic Completion Review and Evaluation ..............................................................10|
|I I I .<br>Dia|gnostic Questions for E valuating Public I nvestment E fficiency ............................12|
|1.|Investment Guidance, Project Development, and Preliminary Screening ...............12|
|2.|Formal Project Appraisal .........................................................................................12|
|3.|Independent Review of Appraisal ............................................................................12|
|4.|Project Selection and Budgeting ..............................................................................12|
|5.|Project Implementation ............................................................................................13|
|6.|Project Adjustment ...................................................................................................13|
|7.|Facility Operation ....................................................................................................14|
|8.|Basic Completion Review and Evaluation ..............................................................14|
|I V .<br>Con|clusion ........................................................................................................................15|
|R eferences|.....................................................................................................................................16|
|Annex......|..................................................................................... 17|
Policy Research Working Paper 7733

# **Abstract**

Financial regulation affects government revenue whenever it imposes both the mandatory quantity and price of government bonds. This paper studies a banking regulation adopted by the National Bank of Ethiopia in April 2011, which forces all private banks to purchase a fixed negativeyield government bond in proportion to private sector lending. Having access to monthly bank balance sheets, a survey of branch costs and public finances documentation, the effect of the policy on government revenue can be tracked. This is compared to three plausible revenuegenerating alternatives: raising funds at competitive rates on international markets; distorting the private lending of the state-owned bank; and raising new deposits through additional branches of the state-owned bank. Three main results emerge: the government revenue gain is moderate (1.5–2.6 percent of the tax revenue); banks comply with the policy and amass more safe assets; banks’ profit growth slows without turning negative (from 10 percent to 2 percent).

This paper is a product of the Finance and Markets Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at fstrobbe@worldbank.org and N.Limodio1@lse.ac.uk.

_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
of restrictions imposed by the legal and regulatory frameworks. The average years of secondary schooling of the adult population --taken from Barro and Lee (2001)-- to represent educational and skill achievement of the working force. And an index of sociodemographic factors --constructed from the World Bank’s _World Development Indicators_ and other databases-- which includes the share of youth in the population, the share of rural population, and the share of agriculture in GDP.<sup>10</sup> Remarkably, all 16 correlation coefficients (4 informality measures times 4 determinants) are highly statistically significant, with p-values below 1%, and of large magnitude, ranging approximately between 0.54 and 0.87. All informality measures present the same pattern of correlations: informality is negatively related to law and order, regulatory freedom, and schooling achievement; and it is positively related to factors that denote the early stages of socio-demographic transformation.

Therefore, all these explanations may hold some truth in them. What we need to determine now is whether each of them has _independent_ explanatory power with respect to informality. Or, more specifically, we need to assess to what extent each of them is relevant both in general for the cross-section of countries and in particular for a given country. To this purpose we turn next.

In what follows, we use cross-country regression analysis to evaluate the _general_ significance of each explanation on the origins of informality. Each of the four informality measures presented earlier serves as the dependent variable of its respective regression model. The set of explanatory variables is common to all informality measures and represents the major determinants of informality. They are the same variables used in the simple correlation analysis, introduced above. Then, we apply these estimated relationships to the case of the Latin American and Caribbean countries with available data in order to evaluate the _country-specific_ relevance of each proposed mechanism. We can do this for those countries that possess complete information on dependent and explanatory variables, or at least information on the latter, with which we can obtain _predicted_ values of the dependent variable. There are 20 countries in the Latin

> 10 This is constructed by first standardizing each component (to a mean of zero and a standard deviation of 1) and then taking a simple arithmetic average.  We use a composite index, rather than the components separately, given the very high correlation among them.

10
Heckman, J (1976), “ The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models”, Chapter in Sanford Berg Ed. _Annals of Economic and Social Measurement_ , Volume 5, number 4.

Hertz Tom, T. Jayasundera, P. Piraino, S. Selcuk, N. Smith and A. Veraschagina (2007). “The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. The B.E. Journal of Economic Analysis and Policy (Advances), 7(2), Article 10.

Jalan, J. and R. Murgai, (2008). “Intergenerational Mobility in Education in India, Manuscript, World Bank, Delhi.

Lam, David, and Robert F. Schoeni. 1993. “Effects of Family Background on Earnings and Returns to Schooling: Evidence from Brazil.” Journal of Political Economy 101(4):710 40.

Lefgren, L, & Matthew J. Lindquist, & David Sims, 2012. “Rich Dad, Smart Dad: Decomposing the Intergenerational Transmission of Income,” Journal of Political Economy, University of Chicago Press, vol. 120(2), pages 268 - 303.

Lillard, Lee and Robert Willis. 1995. “Intergenerational Educational Mobility, Effects of Family and state in Malaysia”. The Journal of Human resources, Vol. (29), pp 11261166.

Maitra, P and A. Sharma (2010), “Parents and Children: Education Across Generations in India, Working paper, Monash University.

Mazumder, Bhashkar (2005), “Fortunate Sons: New Estimates of Intergenerational Mobility in U.S. Using Social Security Earnings Data,” Review of Economics and Statistics, May, 2005.

Mulligan, Casey B. (1997). _Parental Priorities and Economic Inequality_ . Chicago: University of Chicago Press.

Solon, Gary, 1992. “Intergenerational Income Mobility in the United States,” American Economic Review, American Economic Association, vol. 82(3), pages 393-408, June.

Solon, Gary (1999). “Intergenerational Mobility in the Labor Market, in O. Ashenfel33
WPS4042

# MOBILIZING URBAN INFRASTRUCTURE FINANCE WITHIN A RESPONSIBLE FISCAL FRAMEWORK

# SOUTH AFRICAN CASE

## **Philip van Ryneveld**<sup>_1_</sup>

### World Bank Policy Research Working Paper 4042, November 2006

_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._

> 1 former Chief Financial Officer, Capetown

1
Similarly, and also relying on the Carrington and Detragiache dataset, Faini (2002) compares gross enrollment rates across 51 countries to measures of the extent of high skilled migration to the OECD from these countries, and finds little evidence that a greater rate of emigration of the highly skilled induces greater domestic enrollments in higher education.<sup>15</sup> As with all cross country regressions, the robustness of these studies are severely hampered by the potential for omitted variable bias resulting from cross country heterogeneity. Most recently, Beine, Defoort and Docquier (2007) have gone some way to addressing this problem. And, using a panel of 6 observations per country, have found that the impact of skilled migration on education is contingent on the country level of development, that is, it is only ambiguously perceptible in lower income countries while it is unambiguously imperceptible in middle and higher income countries. There are, however, remaining concerns.

Beine et al (2007) highlight the potential endogeneity of the migration rates of skilled workers with respect to the change in the human capital level. Indeed, stylized facts and empirical findings (Docquier et al 2006) suggest that ceteris paribus and increase in natives’ average level of schooling reduces the skilled emigration rate. This may result from: quota restrictions on the number of educated immigrants accepted, such that the higher the proportion of educated adults, the lower the probability that each of them will be able to leave the country - this is the option discussed in Beine et al (2007), alternatively endogeneity may result from complementarities in skilled labor such that an increase in the educated workforce encourages others to stay, conversely and finally more skilled graduates may reduce the wage premium pushing more to migrate.

Beine et al (2007) correct for this endogeneity using lagged migration rates as an instrumental variable for current migration rates. The assumption behind a substantial proportion of individuals with tertiary education; lastly, the data does not cover the former Soviet Union nor Eastern Europe. As a result of these problems the assumptions (such as the generalisation of the US Census data to apply across the rest of the OECD) made by Carrington and Detragiache cause concerns that significant amounts of data manipulation do little to assuage.

> 15Faini does find evidence of induced increases in secondary education but believes this indicates would be migrants choose to pursue their higher education abroad

12
# 3.3 Implementing the Lasso

The next question of course is how to determine the tuning parameters λ and φ<sup>�</sup> k. As a starting point, the two existing approaches we will first examine are the “plug-in”lasso of Belloni, Chernozhukov, Hansen, and Kozbur (2016) and the traditional cross-validation approach, both of which we have modified to fit the demands of the three-way PPML setting. As we will discuss, each of these methods has its strengths and weaknesses. Therefore, we will then turn to describing two extensions of the plug-in lasso, which we call the “iceberg lasso”and “bootstrap lasso”, that are intended to address one of the plug-in lasso’s key shortcomings in this context.

## Plug-in Lasso

The plug-in lasso is so-named because it specifies appropriate functional forms for the penalty parameters based on statistical theory and then uses plug-in estimates for these parameters. It is therefore a “theory-driven”approach to the variable selection problem, whereas cross-validation, discussed next, is a more traditional machine learning method that relies on out-of-sample prediction. The plug-in lasso was first proposed by Belloni, Chen, Chernozhukov, and Hansen (2012), though the specific implementation we build on is the “cluster lasso”method of Belloni, Chernozhukov, Hansen, and Kozbur (2016), which allows for correlated errors within clusters.

Without delving too much into technical details, which we defer to the Appendix, variable selection using the plug-in lasso can be thought of as involving the following three ingredients:

- i. The absolute value of the score for each βk when evaluated at 0,

- ii. The standard error of the score for each βk,

- iii. Values for λ and φ<sup>�</sup> k set high enough so that the absolute value of the score for βk must be large relative to its standard error in order for regressor xijt,k to be selected.

Intuitively, the value of the score reflects the impact that a small change in βk has on the fit of the model. When evaluated at 0, it tells us how much the fit of the model improves when we make βk non-zero. The standard logic of the lasso is that this improvement in fit must be large relative to the penalty in order for β<sup>�</sup> k to be non-zero. One of the main innovations of the plug-in lasso is to allow the regressor-specific penalty� φk to adjust to reflect the standard error of the score. This way, we counteract the possibility that regressors could be mistakenly selected due to estimation noise rather than because of their true impact on the model. These regressor-specific penalties play an important role in the presence of heteroskedasticity, which of course is an important feature of trade data. Because the provision sets in xijt vary by agreement, and because we expect errors to be serially correlated over time, we use the cluster lasso approach to constructing these weights as in Belloni, Chernozhukov, Hansen, and Kozbur (2016). Specifically, we cluster all observations belonging to pairs that form agreements by the 11
production; the existence of a boutique market of user/growers interested in developing better breeds of the plant; and the ease of entry, since the seeds are widely available and there are probably few economies of scale in growing beyond quite a small number of plants and there is no further processing.

||**1990**|**1995**|**2000**|**2001**|**2002**|**2004**|
|---|---|---|---|---|---|---|
|**Afghanistan**|1,570|2,335|3,276|185*|3,400|4,200|
|**Colombia**|0|71|88|58|50|73|
|**Laos**|202|128|167|134|112|45|
|**Mexico**|62|53|21|71|47|n.a.|
|**Myanmar**|1<br>,621|1<br>,664|1<br>,087|1<br>,097|828|570|
|**Pakistan**|150|112|8|5|5|40|
|**Thailand**|20|2|6|6|9|**|
|**Vietnam**|90|9|**|**|**|**|
|**Other**|45|78|38|40|40|40|
|**TOTAL**|3<br>,760|4<br>,452|4<br>,691|1<br>,596|4<br>,491|4<br>,850|

Source: UNODC (2004, p. 16)

*Reflects production crackdown by Taliban

**Included in other

# **Table 4: Global Production of Opium for Selected Years (in Metric Tons)**

## **TRAFFICKING COUNTRIES**

As with production, the trafficking of coca and opium involves a relatively small number of nations. One indicator of which countries are involved in trafficking is drug seizures but it requires careful interpretation.<sup>13</sup> Seizures can be driven by production, local consumption, and transshipment; nations with large seizures that are neither producers nor major consumers are likely to be nations involved in trafficking to other countries. It is a one-sided indicator; some transshipment nations either as a result of corruption or limited enforcement effort, may have few seizures. Illustrating the weakness of seizure as an indicator, are the figures for Russia. It

> 13 The annual series for most countries are quite noisy because a few large seizures can substantially affect the total.  Over the long term, however, seizure data tends to suggest the actual level of trafficking.  UNODC (2005) has shown that seizures track well an independently generated estimate of total production.

7