rafmacalaba/datause-encoder-data huggingface dataset:
- 13 -

**<u>Table 2. OLS regression results with the small positive number specification</u>**

||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|<br>Std.Err.||
|---|---|---|---|---|---|---|---|---|---|
|ln_A_|-0.029|(0.014)|**|-0.032|(0.014)|**|-0.034|(0.014)|**|
|ln_L_|0.305|(0.026)|***|0.354|(0.025)|***|0.344|(0.025)|***|
|ln_R_|0.099|(0.010)|***|0.099|(0.010)|***|0.100|(0.010)|***|
|ln_I_|0.142|(0.012)|***|0.150|(0.012)|***|0.148|(0.012)|***|
|ln_F_|0.482|(0.020)|***|0.471|(0.020)|***|0.473|(0.020)|***|
|ln_KM_|-0.046|(0.014)|***|-0.069|(0.014)|***|-0.067|(0.014)|***|
|ln_MAI_|0.583|(0.038)|***|||||||
|ln_AGAI_||||0.417|(0.032)|***||||
|ln_AGAI_i_|||||||0.210|(0.063)|***|
|ln_AGAI_o_|||||||0.248|(0.061)|***|
|constant|1.948|(0.299)|***|1.075|(0.277)|***|1.273|(0.280)|***|
|Obs|3,198|||3,198|||3,198|||
|F statistic|742.2|||724.4|||644.87|||
|R-squared|0.703|||0.696|||0.698|||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

**<u>Table 3. OLS regression results with the Battese’s specification</u>**

||<br>OLS<br>Coef.|<br>Std.Err.||<br>OLS<br>Coef.|Std.Err.||OLS<br>Coef.|Std.Err.||
|---|---|---|---|---|---|---|---|---|---|
|ln_A_|-0.039|(0.015)|***|-0.043|(0.015)|***|-0.044|(0.015)|***|
|ln_L_|0.289|(0.026)|***|0.334|(0.025)|***|0.325|(0.025)|***|
|ln_R_|-0.013|(0.030)||-0.034|(0.030)||-0.026|(0.030)||
|ln_I_|0.307|(0.033)|***|0.310|(0.034)|***|0.311|(0.034)|***|
|ln_F_|0.534|(0.031)|***|0.531|(0.032)|***|0.529|(0.032)|***|
|ln_KM_|-0.053|(0.013)|***|-0.074|(0.013)|***|-0.071|(0.013)|***|
|ln_MAI_|0.500|(0.038)|***|||||||
|ln_AGAI_||||0.344|(0.030)|***||||
|ln_AGAI_i_|||||||0.206|(0.056)|***|
|ln_AGAI_o_|||||||0.174|(0.053)|***|
|d_R_|-0.455|(0.107)|***|-0.544|(0.109)|***|-0.517|(0.109)|***|
|d_I_|1.035|(0.116)|***|1.027|(0.118)|***|1.032|(0.119)|***|
|d_F_|-0.087|(0.218)||-0.102|(0.225)||-0.104|(0.224)||
|constant|0.884|(0.290)|***|0.133|(0.272)||0.294|(0.275)||
|Obs|3,198|||3,198|||3,198|||
|F statistic|801.6|||803.5|||736.96|||
|R-squared|0.738|||0.733|||0.734|||

Note: The dependent variable is ln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
**Table 10: Average Educational Attainment of Individuals**<sup>**whoBegan Working at Different Ages**</sup>

|Age<br>B||Urban|||Rural|||Total||
|---|---|---|---|---|---|---|---|---|---|
|egan<br>||||||||||
|Working|Boys|Girls|Total|Boys|Girls|Total|Boys|Girls|Total|
|**4**|4.17|4.24|4.19|2.23|3.02|2.49|3.71|3.93|3.79|
|5|4.68|4.58|4.65|2.15|4.19|2.63|3.90|4.49|4.08|
|6|5.19|4.83|5.05|2.55|3.35|2.80|4.24|4.39|4.29|
|7|4.80|4.77|4.79|2.84|2.82|2.84|4.20|4.22|4.21|
|8|4.73|4.67|4.71|2.57|3.00|2.70|3.99|4.13|4.03|
|**9**|5.13|4.86|5.03|2.54|2.83|2.65|4.29|4.20|4.26|
|10|4.85|5.00|4.90|2.28|2.73|2.44|3.90|4.10|3.97|
|11|5.73|5.43|5.62|2.91|2.80|2.87|4.97|4.70|4.87|
|**12**|5.99|5.82|5.94|3.02|3.30|3.12|5.25|5.16|5.22|
|**13**|6.61|6.48|6.56|3.87|3.74|3.82|6.16|6.02|6.11|
|**14**|7.53|7.53|7.53|3.92|4.02|3.96|7.13|7.07|7.11|
|**15+**|8.42|9.27|8.85|4.51|5.32|4.93|8.10|8.94|8.53|
|**Total**|6.93|7.78|7.29|3.04|3.63|3.27|6.16|7.05|6.53|

Source: Calculations based on PNAD 1996.

# **_Eamings and Poverty_**

38. A child's potential future earnings, as well as future poverty, may<sup>be affectedby his or her</sup> entry into the labor market at a young age but much more so for girls than<sup>boys.In additionto the</sup> physical and mental risks to which these children arc exposed,<sup>whichaffecttheircapacity to work</sup> and earn income in the future, income may be affected<sup>by their lower educationalattainment.While</sup> this does not necessarily indicate a causal relationship, these<sup>datademonstratethat for both boys and</sup> girls, the earlier the age of entry into the labor market the lower<sup>thefutureearnings(seeTable11).</sup> Furthermore, when it comes to earnings, girls are much more likely<sup>to sufferthe consequencesof</sup> early labor force entry than boys. This is especially true in<sup>rural areaswhere girlswho began</sup> working at age 7 earn 55 percent less than if they had begun<sup>workingat age15 or older, whereasfor</sup> boys the difference in only 23 percent. In urban areas,<sup>girlsearn52percent lessand boys32 percent</sup> less than if they had begun working at age 15 or older.

39. Examining the gender wage differential for different ages of entry into the<sup>labor market,</sup> women earn between 64 percent and 83 percent less than men if they began<sup>working betweenthe</sup> 16
relative wages and the distribution of the labor force by sector) and this is usually straightforward when national accounts and labor force surveys are available. The other part, however, refers to unobservable parameters such as the elasticities of substitution in the production functions. One would like, for instance, to estimate the distributions of these parameters to replicate the statistical properties of known time series predicted by the model. Such exercises have actually been conducted in the case of small ̈toy models ̈ (see Abdelkhalek and Dufour, 2006), but are difficult to implement in the case of large scale models such as SSELMA. The model, however, is not intended to be used for prediction purposes. The more modest objective is to help the analyst understand the direction and order of magnitude of changes in output variables resulting from policy interventions, and how these policies interact, based on a set of reasonable assumptions regarding the value of the various parameters.

# **_Initial conditions_**

The model was calibrated from a 2003 database for the Moroccan economy. The social accounting matrix (SAM) has been built on the basis of the national accounts provided by the Moroccan “Direction de la Comptabilité Nationale” and of the data from the labor force survey and the informal sector survey provided by the “Direction de la Statistique.” The SAM distinguishes 32 activities (13 of which are informal) and 19 commodities. It is composed of 5 agents (Households, Firms, Government, Social Security and Rest of the World) and 7 factors (described above). The investment matrix has also been provided by the “Direction de la Statistique”.

In terms of wages, the 1998 household survey was used (unfortunately, the most recent 2002 household survey did not include information on earnings). First, relative wages by skill level were estimated. To this end, the various skills available in the survey were collapsed into the six skills used in the model and outliers drop from the survey. The relative wages where then used to calibrate initial average wages by sector, in order to replicate the total wage bill of the sector given the distribution of skills provided by the labor force survey.

15
- 18 -

manufacturers.6 Assuming that the sources of the revenues were not changed in the 1988-1990 period, the following equation relates the cost-recovery ratios applied to the two sectors in 1988: (13a) x, (0.267) + x2 (0.733) = 0.219, where xi and x2 are the cost recovery ratios of manufacturing and non-manufacturing respectively. If one of these ratios can be estimated, then the other can be calculated from the above equation. We estimate xl from our sampled manufacturers and then use the above equation to calculate x2.

The 160 firms in our sample bought 837,148,000 KWh of power from NEPA in 1987 and collectively paid 15,845,000 naira to NEPA. At an average cost of 0.32 naira per KWh, the power delivered to these firms cost 267,887,360 naira to produce. Dividing payments to NEPA by the cost we get x, = 0.059. Hence, only 5.9% of the cost of serving these manufacturers was recovered by NEPA. From tne above equation, x2 = 0.277, which means that NEPA recovered 27.7% of the cost of serving the nonmanufacturing users. Together, the two sectors recovered 21.9% of NEPA's operating cost. Hence, the nonmanufacturing sector's cost recovery rate in 1988 was 4.695 times that of the manufacturing sector.

In performing simulations, we will experiment with alternative tariffs while keeping the relative cost recovery ratios (or the rates of c.-oss-subsidization) at 1988 conditions. We will also simulate mid-1989 conditions when NEPA achieved full cost recovery, with the average tariff raised to 0.32 naira/KWh, by assuming that the relative cost recovery ratios remained at 1988 levels. For mid-1989, the above equation will be written by setting the right side equal to one

> 6 See page _85_ of World Bank Report No. 11672-UNI.
**,** s

<!-- Start of picture text --> ~~~~~~~~~Policy _ Re euch.<br>Suil ector Mlanagement<br>and Private Sector Development<br><!-- End of picture text -->

**WPS 923**

- This paper a product of the Public Sector Management and Private Sector Development Division, - Country Economics Departnent was prepared for the Department's training seminar on Tools for Private SectorAssessment. Portions of thepaperhave appeared in other CECPS work. Copies ofthispaper are available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contact Priscilla Infante, room N9-059, extension 37642 (June 1992, 19 pages plus 9 pages of an annex).

Firm-level surveys elicit information important to formulating sound policy advice and designing projects to promote private sector development. Drawing on recent World Bank experidnce in eight countries, Stone advises why and how to implement targeted field surveys. He answers six questions:

* Why use targeted field surveys?

• How should surveys be focused and designed?

responses relevant for policy formulation. He gives special emphasis to ranked questions, which have proven valuable in focusing attention on the constraints on operations and growth that firms find most binding.

He also identifies pitfalls that diminish the value of surveys and bias their results. And he discusses such practical necessities as training and supervising local surveyors, identifying firms, and entering data into an appropriate software package.

* What types of questions work best in surveys?

* How can surveys be oriented toward their target population?

* How should surveys be implemented?

Finally, Stone cautions against two extremes: omitting assessments of the private sector (thus ignoring the views and experiences of firms) or using survey results out of context, and failing to weigh them against other sources of information. Omission is the more serious of the two problems.

* How should responses be analyzed and used?

In this common sense guide, Stone emphasizes how a carefully designed and implemented survey helps you get the most from a brief session with an entrepreneur or senior manager. Stone gives examples of questions that have been useful in eliciting analytically tractable The firm-level survey is based on the premise that firms have unique insights into the problems of private enterprise because of their daily encounters with their country's policies and institutional environment. Surprises found in recent work- suggest the importance of these surveys in focusing a strategy for private sector development on the most constraining features of that environment.

The Policy ResearchWorkirg Paper Series disseminats the fndings ofwork under way in theBanL Anobjectiveof the series is to get these findings **out** quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
- Kim, Jinyoung, Sangjoon John Lee & Gerald R. Marschke (2009). “International Knowledge Flows: Evidence from an Inventor-Firm Matched Data Set”. In Richard B. Freeman & Daniel Goroff (editors), _Science and Engineering Careers in the United States: An Analysis of Markets and Employment_ , pp. 321–348. Chicago, IL: University of Chicago Press and NBER

- Knack, Stephen & Philip E. Keefer (1997). “Does Social Capital Have an Economic Payoff? A Cross-Country Investigation”. _Quarterly Journal of Economics_ 112(4) (November): 1251–1288

- Krugman, Paul R. (1979). “Increasing Returns, Monopolistic Competition, and International Trade”. _Journal of International Economics_ 9(4) (November): 469–479

- Larra ́ın, Felipe B., Luis F. L ́opez-Calva & Andr ́es Rodr ́ıguez-Clare (2001). “Intel: A Case Study of Foreign Direct Investment in Central America”. In Felipe B. Larra ́ın (editor), _Economic Development in Central America: Growth and Internationalization_ , volume 1 of _Harvard Studies in International Development_ . Cambridge, MA: Harvard University Press

- Maddison, Angus (2003). _The World Economy: Historical Statistics_ . Paris, France: Organisation for Economic Co-operation and Development

- Mankiw, N. Gregory, David Romer & David N. Weil (1992). “A Contribution to the Empirics of Economic Growth”. _Quarterly Journal of Economics_ 107(2) (May): 407–437

- Mansfield, Edward D. (1993). “Concentration, Polarity, and the Distribution of Power”. _International Studies Quarterly_ 37(1) (March): 105–128

- Markusen, James R. (2004). _Multinational Firms and the Theory of International Trade_ . Cambridge, MA: MIT Press

- McCraw, Thomas K. (2010). “Immigrant Entrepreneurs in U.S. Financial History, 1775–1914”. _Capitalism and Society_ 5(1) (January): Art 3

- Melitz, Marc J. (2003). “The Impact of Trade on Intra-Industry Allocations and Aggregate Industry Productivity”. _Econometrica_ 71(6) (November): 1695–1725

- Meltzer, Allan H. & Scott F. Richard (1981). “A Rational Theory of the Size of Government”. _The Journal of Political Economy_ 89(5) (October): 914–927

- Moreno, Ramon & Bharat Trehan (1997). “Location and the Growth of Nations”. _Journal of Economic Growth_ 2(4) (December): 399–418. URL `http://ideas.repec.org/a/kap/jecgro/v2y1997i4p399-418.html`

- Murdoch, James C. & Todd Sandler (2002). “Economic Growth, Civil Wars, and Spatial Spillovers”. _Journal of Conflict Resolution_ 46(1) (February): 91–110

- Nye, Joseph S., Jr. (2004). _Soft Power: The Means to Success in World Politics_ . New York, NY: PublicAffairs

- Obstfeld, Maurice (2009). “International Finance and Growth in Developing Countries: What Have We Learned?” _IMF Staff Papers_ 56(1) (April): 63–111

- OECD (2010). _Perspectives on Global Development 2010: Shifting Wealth_ . Paris, France: Organisation for Economic Co-operation and Development

- Oettl, Alexander & Ajay K. Agrawal (2008). “International Labor Mobility and Knowledge Flow Externalities”. _Journal of International Business Studies_ 39(8) (December): 1242–1260

28
<!-- Start of picture text --> Relative Risk Aversion Parameter: 10 Relative Risk Aversion Parameter: 100<br>U.S. Investor U.S. Investor<br>300 0.2% 45 0.2%<br>250 0.0% 40 0.0%<br>200 -0.2% 35 -0.2%<br>30<br>150 -0.4% -0.4%<br>25<br>100 -0.6% -0.6%<br>20<br>50 -0.8% -0.8%<br>15<br>0 -1.0% -1.0%<br>10<br>-50 -1.2% 5 -1.2%<br>-100 -1.4% 0 -1.4%<br>-150 -1.6% -5 -1.6%<br>Japanese Investor Japanese Investor<br>250 0.2% 15 0.2%<br>200 0.0% 10 0.0%<br>150 -0.2% 5 -0.2%<br>0<br>100 -0.4% -0.4%<br>-5<br>50 -0.6% -0.6%<br>-10<br>0 -0.8% -0.8%<br>-15<br>-50 -1.0% -1.0%<br>-20<br>-100 -1.2% -25 -1.2%<br>-150 -1.4% -30 -1.4%<br>-200 -1.6% -35 -1.6%<br>Optimal Portfolio Shares (LHS) Realized CA/GNP (RHS) Optimal Portfolio Shares (LHS) Realized CA/GNP (RHS)<br>Percent Percent<br>Percentage of GNP Percentage of GNP<br>Jun-60 Jun-62 Jun-64 Jun-66 Jun-68 Jun-70 Jun-72 Jun-74 Jun-76 Jun-78 Jun-80 Jun-82 Jun-84 Jun-86 Jun-88 Jun-90 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04 Jun-60 Jun-62 Jun-64 Jun-66 Jun-68 Jun-70 Jun-72 Jun-74 Jun-76 Jun-78 Jun-80 Jun-82 Jun-84 Jun-86 Jun-88 Jun-90 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04<br>Percent Percent<br>Percentage of GNP Percentage of GNP<br>Jun-60 Jun-62 Jun-64 Jun-66 Jun-68 Jun-70 Jun-72 Jun-74 Jun-76 Jun-78 Jun-80 Jun-82 Jun-84 Jun-86 Jun-88 Jun-90 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04 Jun-60 Jun-62 Jun-64 Jun-66 Jun-68 Jun-70 Jun-72 Jun-74 Jun-76 Jun-78 Jun-80 Jun-82 Jun-84 Jun-86 Jun-88 Jun-90 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04<br><!-- End of picture text -->

**Figure III. Optimal Portfolio Share Abroad by Investor Type**

This figure plots the actual bilateral current account and the model-predicted optimal portfolio shares of foreign investments by investor type, from 1960 to 2005. Expected returns are calculated according to the second specification of the VAR system. We plot the results for relative risk aversion coefficients of 10 and 100. The actual bilateral current account is scaled by U.S. GNP.
(organizing the class, setting the agenda, or disciplining students) then the corresponding number was 77 percent. Once again, the remote councils performed worse than others: Only half of all teachers were engaged in teaching or classroom management in Pujehun, while the corresponding numbers for some other councils were 90 percent.

As a part of baseline data collection, students in Grades 3 (8,740 students) and 4 (8,815 students) were given multiple-choice mathematics and literacy tests. For mathematics, 49 percent of Grade 3 students and 32 percent of Grade 4 students could not answer the single-digit subtraction question “Calculate 9-4”. Similarly for literacy, when presented with the word “bird”, 45 percent of Grade 3 students and 35 percent of Grade 4 students could not identify which one of four four-letter words provided was identical to it. Samples of results from the math and literacy tests are summarized in Figures 2a and 2b.

A student in grades 4 or 5 requires four textbooks: math, English, social studies, and science. At baseline, students only had 2 textbooks on average, far from the government stipulated level of 4 textbooks per student. Further, only about 40 percent of the teachers claimed that they allowed students to take textbooks home, which implies that student access to textbooks at home is very low. To complement this, at endline, around 36 percent of the sampled schools reported parents complaining about lack of textbooks in the last six months. The likelihood of parents complaining about a lack of learning materials is not systematically related to the existence of a school management committee or to schools charging fees.

# **5. Intervention Implementation**

To determine how well the intervention was implemented, two sources of data are used: (i) information from the head teacher at endline and (ii) data on textbook distribution collected by the IE team from district education officers and service providers.<sup>4</sup> Using these data we determine how many schools actually received books from the project.

As a second step, head teacher data are used to determine if the school received textbooks from other sources (such as from local government, non-government

> 4 Head teacher data are given precedence; administrative data are used when head teacher data are missing. In case head teacher and administrative data contradict each other, head teacher data are used, except in 26 cases where the head teacher claims the school received books from the project but is unable to provide information on the number of books received or date books were received.

9
**Table 5. Average income, by source**

||**Ye**|**ar**|**Percen**<br>|**tage**<br>|
|---|---|---|---|---|
|**Income source**|**2005**|**2012**|**change**<br>**2005-2**|**(%)**<br>**012**|
|Average wage/salary agricultural income (per<br>wage/salaried agricultural worker)|170.5<br>|209.4<br>|22.8<br>|***|
|Urban|49.5|57.3|15.8||
|Rural|220.5|272.1|23.4|***|
|Average wage/salary non- agricultural income (per<br>wage/salaried non-agricultural worker)|1243.0|1694.4|36.3|***|
|Urban|2115.0|2799.5|32.4|***|
|Rural|883.3|1238.5|40.2|***|
|Average self-employed agricultural income (per self-|351.2|521.6|48.5|***|
|employed agricultural worker)|||||
|Urban|92.3|140.6|52.3|***|
|Rural|458.0|678.8|48.2|***|
|Average self-employed non- agricultural income (per<br>self-employed non-agricultural worker)|511.2|643.7|25.9|***|
|Urban|913.0|1122.9|23.0|***|
|Rural|345.4|446.0|29.1|***|
|Average income, other sources (per adult)|45.5|73.4|61.3|***|
|Urban|80.9|125.8|55.6|***|
|Rural|31.0|51.8|67.4|***|
|Average remittances (per capita)|24.4|53.8|120.0|***|
|Urban|13.5|36.8|171.6|***|
|Rural|28.9|60.8|110.0|***|
|Average benefits (per capita)|2.5|10.0|293.9|***|
|Urban|1.9|6.8|255.3|***|
|Rural|2.8|11.3|304.8|***|

* Significant at ten percent; ** significant at five percent; *** significant at one percent. Note: Income sources are in rupees, constant prices of 2005, spatially deflated. Source: IHDS, 2005 and 2012.

**Table 6. Share of each income source in total income, household level**

|**Income source**<br>**(% of total household income)**|**Ye**<br>**2005**|**ar**<br>**2012**|**Cha**<br>**2005**|**nge**<br>**-2012**|
|---|---|---|---|---|
|Agricultural wage/salary income|15.9|12.2|-3.7|***|
|Urban|3.8|2.3|-1.6|***|
|Rural|20.9|16.3|-4.5|***|
|Non-agricultural wage/salary income|41.3|44.8|3.5|***|
|Urban|61.9|62.1|0.2||

16
30 introducing monetary and tax adjustments. He also moved to reduce terrorism and capture the terrorist leaders.'<sup>8</sup> He instituted these changes by decree, ignoring legislative and judicial obstacles and bypassing the traditional ministries and strengthening the Ministry of the Presidency (MIPRE). Tensions with the legislative and judiciary rose throughout this period and these two branches formed the principal opposition to the reform program.

This approach paid off both economically and politically. Economically the most important change was a rapid drop in inflation, from a monthly rate of 398 percent in August 1990 to 4 percent by the end of 1992. The decline in GDP was halted and then reversed; GDP grew by over 6 percent in 1993 and over 13 percent in 1994.19 The reforms had negative effects on the poor, however.

The political effects were strongly positive. After an initial drop in popularity, public approval of President Fujimori rose -- from 30 percent shortly after the election to 60 percent in November of 1991 (Apoyo Opinion Reports). He won the support of the military, which in April 1992 collaborated when he dismissed the Congress and the Judiciary in a so-called _autogolpe,_ or self-inflicted coup d'etat. The _autogolpe_ consolidated the President's power and added to his popularity. It was supported by 80 percent of the population according to polls (Apoyo Opinion Reports). The capture of the leader of the Shining Path terrorist organization (Abimael Guzman) and a decline in

> 18 Culminating in the capture of Abimael Guzman, top leader of the Shining Path or _Sendero Luminoso,_ in September of 1992.

> **19** The GDP growth rates were:

|**YEAR**|**%**|**YEAR**|**%**|
|---|---|---|---|
|1987|8.5|1991|7.0|
|1988|- 8.4|1992|- 1.8|
|1989|-11.7|1993|6.4|
|1990|- 5.4|1994|13.1|