rafmacalaba/datause-encoder-data huggingface dataset:
TABLE A.7 Test for Endogenous Matching From Students to Target Teachers

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||||PanelA:A|llstudents|||
|||_Y2_|||_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction of students with a target teacher|0.56|0.55|0.01|0.55|0.53|0.03|
||[0.48]|[0.48]|(0.02)|[0.47]|[0.48]|(0.02)|
|Observations|89,997|172,952||74,580|147,834||
|||Panel B|: Students with|asset index _>_|median||
|||_Y2_|||_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction of students with a target teacher|0.57|0.57|-0.01|0.56|0.55|0.01|
||[0.48]|[0.48]|(0.02)|[0.47]|[0.48]|(0.02)|
|Observations|35,935|67,003||31,501|60,121||
|||Panel C<br>_Y2_|: Students with|asset index _≤_|median<br>_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction of students with a target teacher|0.55|0.53|0.02|0.55|0.52|0.04<sup>_∗_</sup>|
||[0.49]|[0.49]|(0.02)|[0.47]|[0.48]|(0.02)|
|Observations|54,062|105,949||43,079|87,713||
|||Panel D: <br>_Y2_|Students with _T_|_−_1 test scor|e _≤_mean<br>_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction of students with a target teacher|0.56|0.54|0.01|0.52|0.49|0.03|
||[0.49]|[0.49]|(0.03)|[0.48]|[0.48]|(0.02)|
|Observations|20,247|39,699||32,032|63,897||
|||Panel E: <br>_Y2_|Students with _T_|_−_1 test scor|e _>_ mean<br>_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction of students with a target teacher|0.51|0.54|-0.03|0.55|0.54|0.02|
||[0.48]|[0.49]|(0.03)|[0.47]|[0.48]|(0.02)|
|Observations|21,306|41,063||31,684|59,287||

_Notes:_<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Panel A tests whether there is differential matching from students to target teachers. Panels B - E test whether there is differential matching, conditional on socioeconomic characteristics (panel B and C), or on test scores in the previous period (panel D and E). As proxy for socioeconomic characteristics we use an asset index. Students were asked 7 simple questions on the availability of different assets. Specifically, they were asked whether there is a TV, a fridge, a hand phone, a bicycle, a motor bike, a car, or a computer at their home. The asset index is the fraction of the available items and may take on values from 0 to 1. The results suggest that there is no differential matching of target teachers to students. School-level clustered standard errors reported in parentheses. Within-group standard deviations reported in brackets.
# Infrastructure and Structural Change in the Lake Chad Region

## Mathilde Lebrand<sup>*****</sup>

**Keywords:** infrastructure, general equilibrium, transport corridors, structural change **JEL Classification: F15, J24, L16, O13, O14, O18, Q41, R1**

> *World Bank (E-mail: mlebrand@worldbank.org). The paper benefited from the review of Erhan Artuc (Senior Economist, DECTI), and was presented at several internal seminars (Infrastructure Seminar ’InfraExchange’ 2020, workshop for the Lake Chad Economic Memorandum). We thank Kezhou Miao and Han Byul Lee for excellent research assistance. We also thank Matías Herrera Dappe, Niclas Moneke and Immo Schott for their guidance as well as participants at workshops for helpful comments.
17 function is thus insufficiently flexible to test for economies of scope in a multiproduct environment.

# 3.4 Too many hospitals in Kenya?

Ignoring for the moment the long-run nature of economies of scale, in the case of eq (19') we have _S=_ 1 + a 2 + C3 - _C4_ 0.2 and SOUM = a 4 = 0.3 so that both outputs have product-specific diseconomies of scale. Given the result in eq (18) above, we would expect ray economies of scale to exist if these product-specific economies are sufficiently strong to offset the assumed diseconomies of scope. In fact SN = 1 / (1 + a2 + a3 ) = 2.0.

This does not takes into account that the estimated equation is not a long-run equation, but rather a variable cost function. Because of this it is more appropriate to compute SN using eq (14') rather than eq (14). Ideally 6lnCv/6lnK would be evaluated at K, but since there is insufficient information in Anderson's paper to calculate KX, we evaluate SN at the actual value of K. This gives SN = (1-a, + a 2)/(a 4 + (1 + a2 + a3 - a 4 ) = 1.55, implying mild ray economies of scale.''6 The implication is that the ray average cost curve is downward sloping and that product-specific economies of scale are also larger than the short-run estimates above suggest. However, as indicated above, the fact that ray economies exist in a multiproduct setting does not necessarily mean that there ought to be fewer hospitals. Moreover, given the restrictions in the estimated equation, the fact that SN points towards ray economies of scale ought to be treated with some caution.

# 4. Hospital costs in Peru

Dor (1987) reports the results of a cost function estimated on data for 19 urban public hospitals in Peru in 1984. His equation bears a close resemblance to the equations estimated by Feldstein (1967) and takes the form (20) C/A = _ao_ + _a,F_ + a2F<sup>2</sup> + a3OUTP + a4%DEL + a5%SURG + adMIN + v, l. Anderson also concluded that hospitals in his sample exhibited economies of scale. However, his conclusions were based on the value of the coefficient on the stock of beds, which, as we argued above, bears on the Issue of over-capitalization rather than economies of scale.
series. Since the PWT series run from 1950 to 2000, the resulting estimates for those boundary years contain an element of further guesswork, which is based on inspection of the entire series. The smoothed estimates are reported in Table 1.

Starting with _t_ = 50 in (3), we need _λ_<sup>_a_</sup> 10<sup>,. . . ,</sup><sup>_λa_</sup> 50<sup>:thisisthedeepestforaybackinto</sup> the 20<sup>_th_</sup> Century. At _t_ = 10, apart from a few colonial administrators, missionaries and white settlers, not even the youngest adults had any education at all (Sheffield, 1973; Thias and Carnoy, 1972). This may be termed a ‘state of economic backwardness’, in which, by definition, _λ_<sup>_a_</sup> = 1 for all age-groups. Hence, we anchor the system to _λ_<sup>_a_</sup> 10<sup>= 1.ThehistoryofeducationinKenyaalsoleavesnodoubtthat</sup><sup>_λ_2</sup> 20<sup>wasscarcely</sup> above unity, so we shall set it exogenously, at a value to be determined in the light of the series for _e_<sup>_a_</sup> _t_<sup>(</sup><sup>_a_= 1</sup><sup>_,_2).</sup> To obtain _λ_<sup>_a_</sup> _t_<sup>for</sup><sup>_t_=30onwards,only(1)needcomeintoplay.Forthefactthat</sup> _e_<sup>2</sup> _t_<sup>=0uptoandincluding2000impliesthat</sup><sup>_λ_3</sup> _t_ +10<sup>=</sup><sup>_λ_2</sup> _t_<sup>_∀t≤_100.Sincewehaveset</sup> _λ_<sup>_a_</sup> 10<sup>and</sup><sup>_λa_</sup> 20<sup>exogenously and have estimates of</sup><sup>_e_1</sup> _t_<sup>for</sup><sup>_t_= 20 onwards from the Censuses,</sup> we can employ (1) starting with _t_ = 30 if we have estimates of the relative sizes of the age-groups 15-24 and 25-34, respectively. In 1950 and 1960, the said ratio was 57:43, which we assume to have held also in the earlier decades of the 20<sup>_th_</sup> century. Thus, with the above assumptions, we can indeed obtain _λ_<sup>2</sup> _t_<sup>from</sup><sup>_t_= 30 onwards by choosing</sup> the plausible value _λ_<sup>2</sup> 20<sup>= 1</sup><sup>_._01.</sup> Since (1) is a purely technical relationship and (3) is effectively an identity, we can employ both for the entire historical period 1950-2000, even though AIDS had begun to make itself felt by 1990. Eqs. (1) and (3) then yield 14 equations in the following 26 unknowns: ( _λ_ 30, . . . , _λ_ 100); ( _α_ 50, . . . , _α_ 100); ( _z_ 20<sup>1,. . . ,</sup><sup>_z_</sup> 90<sup>1);</sup><sup>_ε_,</sup><sup>_γ_.Somerestrictions</sup> on the latter are therefore unavoidable if we are to solve the system.

We employ the following criteria to define the restrictions and select the solution. First, we use additional information to identify the possible points in time when struc18
-8-

example of one such family of measures,<sup>see Foster, Greer and Thorbecke,</sup> 1984).

To summarize, it seems to us that the best strategy<sup>in measuring poverty</sup> is to start with a distribution of<sup>individuals by per capita household</sup> consumption or income, information<sup>that is available for many developing</sup> countries, and which will become available<sup>for more and more countries as the</sup> decade progresses. We should then<sup>use a range of country-specific poverty</sup> lines, chosen so as to command consensus,<sup>and calculate not only the headcount</sup> index but other indices that emphasize<sup>the depth of poverty. Equally</sup> important, and strongly complementary<sup>to the consumption/inconme based measures</sup> of poverty, should be measures of<sup>achievement along dimensions such as health,</sup> nutrition and education. We are<sup>as yet some way from arriving at a single</sup> measure of the standard of living<sup>that combines these various dimensions with</sup> consumption. However, in some cases<sup>data may permit examination of the non-</sup> consumption achievements of those<sup>classified as poor on consumption grounds.</sup> In other cases we may be restricted to looking<sup>at achievements in health,</sup> education, etc., only at the national<sup>level.</sup>

# 3. Evolution of Povertv in the Developing<sup>World</sup>

Through the empirical -:.nalysis in the WDR and the HDR, a consensus emerges on the broad quantitative magnitudes<sup>of poverty in developing</sup> countries, and on the evolution of this<sup>poverty over the past 30 years. Both</sup> reports arrive at a figure of over<sup>one billion people below the poverty line.</sup> In the case of the WDR this line is specified<sup>as $370 per capita per year in</sup> 1985 purchasing power parity prices<sup>--</sup> in other words, a little over a dollar a day.
-94 -

_-_ demand forecast

- project costs

- economic justificatlen of the project

# - financial performance - debt service

## - required tariff Increases, e.g. fuel adjustment clauses - avall bility of funds for the project

At the sector level, there Is a need to constantly review the power sector Investnent program where Internatlonal and national economic conditions are changing rapidly. In particular, there Is a need to review demand forecasts (in 60% of cases where actual demand was below the forecast, one of the reasons cited was poor macroeconomic conditlons). If there were large Increases In costs in general and not just for the project, would the Investment program as planned still be justified? If so, would local and foreign exchange funds be available? Risk analysis In this sense Is seen as being more extensive than the simple form of sensitivity analysis normally carried out at appraisal. This Is the sort of analysis that a well establishAd utility would undertake as part of Its medium and long term planning.

Finally, for the Bank itself, there Is a need for a grea!er realism In setting performance targets. PPARs are Intended to focus on the performance of a single project to assess whether the Individual Invest.ment was justified and whether It was Implemented as planned. With this narrow focus, It Is not surprising that the overall trends In the power sector of a country are not reflected. The present focus, however reduces the usefulness of the data which are available from the PPARs as It Is difficuit to get an accurate assessment over time of the power sector In Individual countries In order to determine trends and causal factors. The statistical analysis of the Bank's historical Involvement Is becoming more difficuit as the number of prolects Is Increasing and the data become more difficult to retrieve and manipulate. It Is recommended that greater attention be given to the collection and maintenance of a data base that will serve both audit and operatlonal needs.
Policy Research Working Paper 7400

# **Abstract**

Surveys of export promotion agencies suggest that that they tend to focus on helping firms become exporters as a means to stimulate aggregate export growth. But the existing empirical evidence has paid little attention to the role of export promotion agencies in helping entry into exporting. This paper fills this gap with a panel of exporting and non-exporting firms from seven Latin American countries during the period 2006-2010. The results suggest that export promotion encourages exports mainly by helping firms enter into and survive in export markets. The impact on the intensive margin of exporting firms is not robust. This finding is consistent with export promotion helping reduce fixed rather than variable costs of exporting, which is to be expected if export promotion agencies help correct for market failures associated with information externalities.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 dlederman@worldbank.org and Marcelo.Olarreaga@unige.ch.

_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
# TABLE OF CONTENTS

||FOR|EWORD||
|---|---|---|---|
|I.|TNTR|ODUCTION. . . . . . . . . . . . . . . . . . . . . . . . .||
||A.|Scope of the Review .<br>.<br>.<br>. . . . . . . . . . . .||
||B.|A Historical Perspective on the World Bank's Work||
|||in Population . . . . . . . . . . . . . . . . . . . . . .|2|
|||1.<br>The Early Years. . . . . . . . . . . . . . . . . . .|2|
|||2.<br>The Berelson Report. . . . . . . . . . . . . . . . .|3|
|||3.<br>The Post-Berelson Report . . . . . . . . . . . . . .|4|
|||4.<br>Formation of the Population, Health and||
|||Nutrition Department.|4|
|||5.<br>The Early Years of PHND. . . . . . . . . . . . . . .|5|
|||6.<br>Fiscal Year 1984 to Present. . . . . . . . . . . . .|6|
||C.|A Framework for Assessing Program Effectiveness . . . . .||
|II.|THE|WORLD-BANK INVOLVEMENT<br>WITH THE DEVELOPMENT OF||
||POP|ULATION POLICY. . . . . . . . . . . . . . . . . . . . . . .|l|
||A.|Policy Dialogue . . . . . . . . . . . . . . . . . . . . .||
||B.|Sector Work .... . . . . .<br>......<br>. . . . . . .<br>.|14|
|||1.<br>Volumeand Regional Distribution of Sector||
|||Work .... . . . . . . . . . . . . . . . . . . .<br>.|14|
|||2.<br>Scope of Sector<br>Reports.<br>. . . . . . . . . . . . . .|16|
|||3.<br>Quality of Sector Work . . . . . . . . . . . . . . .<br> <br>|17|
|||4.<br>Impact of Sector- Work.... . . . . . . . . . . . . .|18|
||C.|The Role of Research. . . . . . . . . . . . . . . . . . .|L9|
|||1.<br>Research Done at Bank Headquarters and Its<br>||
|||Contribution to Projects . . . . . . . . . . . . . .|19|
|||2.<br>The Research Component within Projects . . . . . . .|229|
|III.|PRO|JECTS: DESCRIPTION OF EFFORT . . . . . . . . . . . . . . .|23|
||A.|General Description of Projects . . . . . . . . . . . . .|23|
|||l.<br>Number of Projects .23||
|||2.<br>Lending for Projects and Trends. . . . . . . . . . .|23|
|||3.<br>Regional Breakdown and Trends. . . . . . . . . . . .|25|
|||4.<br>The Population Component of PHN Projects and||
|||the Role of IDA. . . . . . . . . . . . . . . . . . .|27|
||B.|Project Design. . . . . . . . . . . . . . . . . . . . . .|28|
|||l.<br>Nature of Planned Interventions. . . . . . . . . . .|28|
|||a.<br>Balance between Health and Family Planning. . .|28|
|||b.<br>Definition of Target Groups . . . . . . . . . .|29|
|||c.<br>Community Participation . . . . . . . . . . . .|29|
|||d.<br>Construction as a Component of Projects . . . .|30|
|||<br>Rl f Edti d Dd Gti||
|||e.<br>oe o ucaon an eman eneraon||
|||Activities. . . . . . . . . . . . . . . . . . .|31|
|||f.<br>Role of Service Delivery. . . . . . . . . . . .|32|
|||g.<br>Large vs. Small Projects. . . . . . . . . . . .|32|
|||h.<br>Designed Efforts to Improve Management||
|||Systems<br>...|32|
|||i.<br>Monitoring and Evaluation . . . . . . . . . ...|34|
16

**Table 3: Energy as a percent of total expenditures, by expenditure class**

|Expenditure<br>quintile|District<br>heat<br>o|Electricity|Gas<br>|Major<br>energy<br>Isources<br>|Residential<br>coal|Wood|Hot<br>water<br>|All<br>energy<br>|
|---|---|---|---|---|---|---|---|---|
|1(poorest)|i.5<br>|[3.6<br>~|1.3|17.6|]0.4|[0.2|10.4|**[7.4**|
|2|2.2<br>j|3.4|11.7|<br>8.0|J0.8|[0.3|0.6|(9.0|
|3|2.6,|35|2.0|181|11.3|10.3|0.6|1102|
|4<br>t2.6|j|32|12.2o<br>|7.3<br>|2.1<br>|0.2|0.6|**10.8**|
|5 (richest)|12.2j|2.7|J27|16|12.5|{o.i|**0.5**<br>|**[10.3**<br>|
|All|12.2|3.3~|11.|9j7.4|11.4|[0.2|_10.5_|[9.5|

ource: Authors' cabulations from Poland Hous molBu **d** get Survey, 1993s m as t To get an idea of what this means in terms of actual household expenditures, consider that total average monthly expenditures on energy for each group are as follows: the first quintile spent ZI 1,117,740; the second, ZI _1,719,450;_ the third, ZT 2,176,800; the fourth, Zi 2,782,530; and the fifth quintile spent on average ZI 4,729,200 per month. Overall, average monthly household expenditures were ZI 2,517,660 per person. Taking the average annual exchange rate in 1993 of ZI 18,145, the bottom quintile spent ZI 82,712 or about US$ 4.6 on fuel while the top quintile spent ZI 487,108 or about US$ 26.8 on energy, more than _5_ times as much as the bottom quintile.

In terms of the main forms of subsidized energy (district heat, electricity and gas) consumption patterns are distributed somewhat more similarly to income (implying that any subsidies are only slightly regressive). Most of the regressiveness of the energy subsidy is due to gas (the rich consume twice as much as the poor). The subsidy on electricity is flat, except for the highest income group, and on district heat, it is distributed in a u-shaped fashion.

_Income elasticities._ A rough guess at income elasticity can be made simply by looking at the percentage change in energy consumption divided by the percentage change income (Table<sup>4). This</sup>
end of 2 years of the project (Y2), and the third wave was collected in April-May 2012, at the end of 3 years (Y3).<sup>16</sup> Figure II shows the project timeline for the intervention and data collection.

We collected data on school facilities, finances, and other school-level data from head-teacher interviews. Teacher interviews included questions on demographics, experience, pay, outside jobs, income (from teaching and other sources), and job satisfaction. We used a combination of school and teacher interviews to map teachers to specific classrooms and subjects (which will not be needed for the school-level ITT estimates, but will be needed for the IV estimates of the impact of being taught by a certified teacher). Students in all schools were tested using multiple-choice tests of math, science, and Indonesian, and students in junior secondary schools were also tested in English. The tests also included a short demographic survey to collect basic information on household assets from students.

# _III.C. Validity of the Experimental Design_

The randomization was successful in ensuring that treatment and control schools were similar prior to the experiment. There was no significant difference between treatment and control schools on school-level variables such as the number of students, teachers, or class size (Table I - Panel A). There were also no significant differences in student test scores across treatment and control schools on test scores in any subject (math, science, Indonesian, or English) or on an index of household assets (Table I - Panel B).<sup>17</sup> The differences in means in column (3) include "district-triplet" fixed effects, since these triplets are the strata within which we randomized treatment assignment, and the stratum fixed effects will be included in our estimating equation for treatment effects.

Teacher characteristics were also similar across treatment and control schools. There were no significant differences on most teacher-level variables, including teachers' own test scores, their certification status, their base pay, and the incidence of holding an outside job (Table II: Columns 1- 3). The only major difference is that, as expected, teachers in treatment schools were 32 percentage points more likely to have entered the certification quota. This difference confirms that the

> 16 Since the certification process took one year, the first year in which target teachers in treatment schools would have received the additional allowance was the second year of the project.  We felt it was highly unlikely that there would be any impact at the end of Y1 (since teachers in treatment schools would not have received any additional payments at this point). Thus, given the high costs of surveys across the Indonesian islands, we did not collect data at the end of Y1. 17 Note that the randomization (and communication to "target" teachers) was carried out before the baseline survey and hence the randomization could not be balanced ex ante on these variables.  Thus, it is reassuring to see that treatment and control schools were balanced on observables.

14