rafmacalaba/datause-encoder-data huggingface dataset:
INTRODUCTION Xi

# _Vital Statisdcs Report_ and others are estimated from diverse data.

Future trends in life expectancy. Ufe expectancy is projected from year 0 to year t using a logistic function over time of the form e, = ko + k I {1 + exp ilogit(eo) + **rltl,** with - logit(eo) log, ((ko + k - ev) / (e0 ko)I.

The logistic function is set to rise most rapidly from a level of 50 years or so and increasingly slowly at higher levels. 7The minimum (ko) for the logistic functions for both sexes is assumed to be 20 years, and the maxima (ko + k) are assum& - be 82.5 years rar females and 75.8 years for males. The rate of change (r) for the logis.ic fha **£** is allowed to vary across countries, and for a given country to vary over time.

For the first quinque.mnium (1985-90), the rate of char.ge (r,) is estimated from the rate of change in the previous qu:nquennium (ro) and from the female secondary enrolment ratio (sD), using the equations rt = .00379 + .723 ro - .0054 so for females, and rt = .01159 + .885 ro - .000318 so for males.

Percent urban is used in a few cases, with a different equation (see bu.dtao send Bos 1989), where secondary enrolment is not available, and in fewer cases still the rate of change in the previous decade is used instead of the rate of change in the previous quinquennium when the latter appears to have been affected by exceptional circumstances. LUmits are imposed on the rate of change for the first quinquennium (and for aU other quinquenni_), such that it cannot be greater than -.017 (which would give slow mortality decline) or less than -.053 (which would give rapid mortality dec!ine).

For the second quinquennium, the rate of change is estimated as a function of the rate of change for the first quinquennium: r2 = -.007 + .8 rp.

The rate of change for the third quinquennium is estimated from the rate of change for the second quinquennium in the same manner. This equation allows rates of change to converge toward the uniform pattern imposed for subsequent quinquennia.

For these subsequent quinquennia for all countries, the rate of change in life expectancy is constant at -.035 for both s^xes. At this rate, the annual increments to life expectancy vary by ini- : levels as indicated by the medium pattern in Table 1. Minimum and maximum increments corresponding to the slow and rapid limits imposed on rates of change in the first three -.i.'nquennia are also shown.

Future trends in infant mortality. Infant mortality is projected using a similar logistic function. The rate of change for each of the first three quinquennia is obtained from the equation = .03 + .5 r,.1, with the restriction that this rate must be in the range [.024, .1301. Thcse limits provide schedules of minimum and maximum annual decrements to infant mortality, varying by the initial rate, shown
marginal effect in our sample.

An interesting question is whether beyond the significant negative contemporaneous effect of price distortions on economic growth there is also a significant negative lagged effect. A lagged effect could arise if, for example, there are significant adjustment costs to capital that differ across sectors in the economy. Our panel data set has a fairly large _T_ dimension (the average _T_ is about 35) and, therefore, is well suited to explore lagged effects of within-country changes in policy distortions to relative agricultural prices on economic growth. In fact, it is worth to restate here that a key advantage of using annual panel data is that this allows us to examine not only short-run growth effects, which are of substantial interest in and of themselves, but also medium/long-run growth effects by means of a distributed lag model.

In Table 7 we report dynamic panel data estimates as a first approximation to characterize the medium/long-run effect of relative agricultural price distortions on economic growth. It is well known that the presence of country fixed effects leads to inconsistent least-squares estimates in the dynamic panel data specification. Yet in our regressions this fixed effects bias should be relatively small since the average _T_ is fairly large. To check whether this fixed effects bias is indeed small in our regressions, we report, in addition to least squares estimates, system-GMM estimates (Blundell and Bond, 1998). For these system-GMM estimates, we use the first lag as an instrument to ensure that not too many moment conditions are used.

Panel A of Table 7 shows that the dynamic panel data regression produces a coefficient on lagged GDP per capita growth of about 0.05 to 0.1. A test for second-order serial correlation produces insignificant results in all cases. Thus, specification tests indicate that the model is well specified. We compute the long-run growth response from the dynamic panel data model by inverting the characteristic polynomial. This yields a cumulative (long-run) growth effect of a permanent increase in relative agricultural price distortions of about -0.05. This estimate is very similar to the static panel data model where we concentrate on the impact growth effect of relative 17
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 25. Burden of inefficiency of Benin’s power and water utilities**

As a percentage of GDP <!-- Start of picture text --> a. Power sector<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Benin LIC-NoFragile<br>Collection inefficiencies Unaccounted losses<br>Percenatage of GDP<br><!-- End of picture text --> <!-- Start of picture text --> b. Water sector<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Benin LIC-NoFragile<br>Collection inefficiencies Unaccounted losses<br>Percenatage of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Annual funding gap

Benin’s infrastructure funding gap amounts to $210 million per year, or about 5 percent of GDP, once efficiencies are captured. A funding gap is found in the water, power and, to a lesser extent, transport sectors (table 20). By far, the biggest funding gap, even after accounting for recovery of amounts lost to inefficiency, is found in the water sector.

**Table 20. Funding gaps by sector**

<u>US$ million</u>

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Spending needs|(81)|(11)|(222)|(116)|(283)|(712)|
|Existing spending*|81|—|114|105|98|397|
|Reallocation potential within sectors|0|—|0|5|0|5|
|Efficiency gains|17|0|67|0|17|101|
|**Overall funding gap**|**17**|**—**|**(41)**|**(6)**|**(168)**|**(210)****|
|Reallocation potential across sectors|50|—|0|0|0|50|

_Source_ : Derived from Foster and Briceño-Garmendia (2009).

_Note_ : Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. — = data not available. * Traced to needs

** Assuming complete fungibility across sectors

# What else can be done?

The funding gap can be addressed only by raising additional finance or, alternatively, by adopting lower-cost technologies or less-ambitious targets for infrastructure development.

Benin could attract more private finance into its power and transport sectors. The country has received average levels of private finance for infrastructure, compared with its African peers. In the early 2000s, Benin captured private investment commitments worth around 1 percent of GDP, predominantly in the ICT sector (figure 26). Many countries in Africa have done considerably better, and some have 41
# Productivity, Welfare and Reallocation: Theory and Firm-Level Evidence<sup>�</sup>

Susanto Basu Luigi Pascali Fabio Schiantarelli Boston College and NBER Boston College Boston College and IZA Luis Serven<sup>y</sup> World Bank March 1, 2010 JEL: D24, D90, E20, O47 Keywords: Productivity, Welfare, Reallocation, Technology, TFP

> �We would like to thank John Haltiwanger, Chad Syverson and the participants at the NBER Summer Institute, NBER Productivity Lunch, Ottawa Productivity Workshop and Bocconi University seminar for helpful discussions and suggestions.

> yBasu: Susanto.Basu@bc.edu; Pascali: Luigi.Pascali@bc.edu; Schiantarelli: Fabio.Schiantarelli@bc.edu; Serven: Lserven@worldbank.org

1
WPS3839

# **The case for industrial policy: a critical survey***

**Howard Pack** The Wharton School University of Pennsylvania 1400 Steinberg Hall-Dietrich Hall 3620 Locust Walk Philadelphia, PA 19104-6372 E-mail: _packh@wharton.upenn.edu_

**Kamal Saggi** Department of Economics Southern Methodist University PO Box 750496 Dallas, TX 75275-0496 E-mail: _ksaggi@smu.edu_

**Abstract** : What are the underlying rationales for industrial policy? Does empirical evidence support the use of industrial policy for correcting market failures that plague the process of industrialization? To address these questions, we provide a critical survey of the analytical literature on industrial policy. We also review some recent industry successes and argue that only a limited role was played by public interventions. Moreover, the recent ascendance of international industrial networks, which dominate the sectors in which least developed countries have in the past had considerable success, implies a further limitation on the potential role of industrial policies as traditionally understood. Overall, there appears to be little empirical support for an activist government policy even though market failures exist that can, in principle, justify the use of industrial policy.

## World Bank Policy Research Working Paper 3839, February 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._

*This paper has been partly funded by the Development Research Group of the World Bank.
The empirical evidence shows that poverty is detrimental to long-term economic growth. Using panel data of 85 countries covering 1960 to 2000, López and Servén (2015) find that a 10 percentage-point increase in the poverty rate reduces the GDP per capita growth rate by 1 percentage point. In particular, an increase in the poverty rate reduces the investment rate for countries with low levels of financial development. There is also evidence that the negative impact of poverty on growth depends on the initial level of poverty. In a sample of 156 countries covering 1960 to 2010, Marrero and Servén (2018) find that for low levels of poverty (below the median), poverty has an insignificant impact on growth (Figure 8). In contrast, when the poverty rate is high, a 10 percentage-point decrease in headcount poverty is associated with an increase in economic growth ranging from 1 to 2 percent per year.

Related evidence comes from the observation that despite the global reduction in poverty rates, cross-country evidence indicates a lack of convergence in poverty rates. Studying 90 developing countries during the 1991–2004 period, Ravallion (2012) finds that two distinctive effects prevented the convergence of poverty rates. First, poverty reduces growth, consistent with the results from López and Servén (2015). Second, high initial poverty dulls the impact of growth in reducing poverty. The combination of these two channels makes it more difficult for the poorest countries to reduce their poverty rates.

23
# **Figure 5 Learning deprivation gap simulations results**

<!-- Start of picture text --> 3.5<br>3.0<br>2.9<br>2.6 2.7<br>2.5<br>2.4<br>2.0 1.9<br>1.5<br>1.0<br>0.8 0.8 0.8<br>0.5 0.5 0.4 0.6<br>0.0 0.1<br>WLD EAP ECA LAC MNA SAR* SSF<br>10.4 4.4 21.4 9.4 19.2<br>Baseline 1.3 6.2<br>Intermediate Optimistic Pessimistic<br>Change Learning Gap<br><!-- End of picture text --> Source: Author's calculation. All underlying numbers can be found in table A.2 in the annex. The difference between learning poverty and learning deprivation is that learning poverty includes out-of-school children. (*) all countries report LP using NLAs and there for and aggregated gap measures was not computed.

Severity measures can distinguish among these qualitatively different types of impacts. Results for Latin America, the Middle East and Sub-Saharan Africa suggest that on average, sudents in those regions are experiencing a similar increase on the learning gap, of approximatly 2.5 percentage points. However, as discussed in sections 2 and 4, the learning gap fails to take into account the inequality of learning among the learning deprived. This means, that a hypotetical reduction of 20 learning points is fully equivalent, regardless if it happens for students just below the MPL or at the very botoom of the learning distribuiton. This can hide significant differnces on the complexity of the challenge. Figure 6 introduces the results through the lens of the learning severity mesaure introduced in section 2, an indicator which is sensitive to differences in the learning gap-inequality. Two distinct groups emerge from this analysis. Latin America with a learning severity increase of 0.5 percentage point, and the Middle East and Afirca, with an increase greater than one percentage point. The results support the idea that the consequence of this crisis is qualitatively different between Africa and the Middle East, and Latin America. Suggesting that students in the latter group might fall much further, relative to the MPL, than the ones in the former group.

In other words, in absolute terms, Sub-Saharan Africa and the Middle East and North Africa remain the two regions that face the greater challenge to reduce learning deprivation, because of both the magnitude of the problem as well as the heterogeneity of their respective learning deprived students. While, in Latin America, the new learning poor seem to have fallen much closer to the MPL. Moreover, the depth of learning deprivation in Sub-Saharan Africa could increase three times more than the number of new learning deprived children in the region. This is almost three times the global average, and four times more than in Europe and Central Asia. All these results suggest an increase in the complexity and the cost to tackle the learning crisis on the continent.

28
WPS3186

# Donor Fragmentation and Bureaucratic Quality in Aid Recipients

## **Stephen Knack** *

## **Aminur Rahman** **

### World Bank Policy Research Working Paper 3186, January 2004

_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._ ________________________________________

*Address correspondence to Stephen Knack, Senior Research Economist, World Bank, 1818 H St. NW, Washington DC 20433. Email: <u>sknack@worldbank.org. phone: 202-458-9712. fax: 202-522-1154.</u> **Economics Department, University College London, Gower Street, London WC1E 6BT. Email: _<u>rahman.aminur@ucl.ac.uk</u>_ <u>.</u>

This paper was funded as background work for the 2004 World Development Report. The authors are grateful to Virginia Yee for information on and assistance with the AiDA data base, to Claudio Montenegro and Denis Nikitin for assistance with the OECD-DAC aid data, and to Ritva Reinikka, Omar Azfar, Bertin Martens, Poul Engberg-Pedersen and Bill Shaw for valuable discussions and comments.
entrepreneurs. Preferential treatment was provided to micro- and small-scale agricultural producers and small-scale manufacturing to encourage the use of local raw materials and labor. Sixty percent of the outlays for the CF’s were allocated to the Northeast, and 20 percent were allocated to each of the North and the Center West. Funds are transferred from the National Treasury to the Ministry of Integration (“Ministério da Integração”), which later reallocates them to the operating banks – the Banco do Nordeste (FNE), Banco da Amazônia (FNO) and Banco do Brasil (FCO). The CF’s are financed by receipts from income taxes and taxes on industrial products.

Interest rate subsidies are the main incentive offered through the CF’s. While market interest rates offered to private firms are currently more than 45%, the CF’s offer credit at 8.75% to non-agricultural micro firms; 10% to small firms, 12% to medium sized firms; and 14% to large enterprises. Interest rates are even lower for comparable agro producers: 6% for mini-producers, 8.75% for small to average and 10.75% for large ones. These interest rates were negative in real terms in 2002, when inflation was 12.5%. Rates offered to individual producers varied by sector, investment size, and credit record of the borrower. Between 1989 and 2002, more than US $10 billion were provided in subsidized loans, which is about 0.8 % of the total GDP of the 3 regions per year (Ferreria 2004).

# **3. Data**

We now describe the data used in the econometric exercises. We created a working database that maps the spatial entry decisions of new establishments in Brazil, with the goal of learning how the CF’s affect those decisions. The main source of information is 8
million people. Overall, the majority of the inflation data in PovcalNet comes from the World Development Indicators database, and even in those cases where they differ, the differences are not large, but nonetheless the 15-country reference approach is highly sensitive to these differences.

To address these concerns, we propose a methodology for constructing a global poverty line that is less sensitive to inflation updating, more robust to the small-sample problems stemming from the 15-country approach, and also significantly increases the coverage of the population of poor people. Our proposed approach is based on estimating implicit national poverty lines by combining poverty headcounts at national poverty lines as reported in the World Development Indicators with the corresponding consumption and income distributions from PovcalNet.<sup>21</sup> Because the consumption and income distributions we use are all expressed in per capita PPP terms, we can identify the unique poverty line in PPP dollars that corresponds with the reported headcount for the national poverty line.<sup>22</sup> Following this approach allows us to greatly increase the set of countries for which we have national poverty thresholds (thereby increasing the support of our estimate) and also produces a series of poverty lines that are closer to the ICP reference year (thereby reducing the sensitivity of the estimate to errors and updates in inflation data).

More specifically, we extract poverty headcounts from the World Bank’s series of poverty headcount ratios at national poverty lines in its Poverty and Inequality Database.<sup>23</sup> This data set

> 21 For the purposes of our analysis, we use the same fitted distributions as used in the other parts of this paper. In principle, one could use the actual consumption data for those countries where we have this. See the annex for more details on how we generate our fitted consumption and income distributions.

> 22 An attribute of this approach is that it allows us to identify the national average poverty line even in those countries where no national line exists, but only regional lines or lines for household types.

> 23 The series is called _Poverty headcount ratio at national poverty lines (% of population), including noncomparable values (SI.POV.NAHC.NC)_ . National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted

17
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Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Data by income quintile for the Philippines are from the 2015 National Nutrition Survey. Data for Vietnam and Thailand are from World Bank country programs. Countries are sorted by the overall national stunting prevalance. Data correspond to the year with the most recently available stunting estimates by wealth quintile for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2005 (Belarus) and the most recent estimates are from 2016.