rafmacalaba/datause-encoder-data huggingface dataset:
- Das, Jishnu, Stefan Dercon, James Habyarimana and Pramila Krishnan. 2007. “Teacher Shocks and Student Learning: Evidence from Zambia.” _The Journal of Human Resources_ 42(4):820-862.

- Dobbie,W., and Fryer Jr, R. G. (2013). Getting beneath the veil of effective schools: Evidence from New York City. _American Economic Journal: Applied Economics_ 5(4): 28–60.

- Halpin, Peter F., and Michael J. Kieffer. 2015. “Describing profiles of instructional practice: A new approach to analyzing classroom observation data.” _Educational Researcher_ 44(5): 263-277.

- Hamre, Bridget K., Robert C. Pianta, Andrew J. Mashburn, and Jason T. Downer. 2007. “Building a science of classrooms: Application of the CLASS framework in over 4,000 US early childhood and elementary classrooms.” _Foundation for Childhood Development_ .

- Hanushek, Eric A. and Ludger Woessmann. 2008. “The Role of Cognitive Skills in Economic Development.” _Journal of Economic Literature_ 46(3): 607-68.

- Hanushek, Eric A. and Ludger Woessmann. 2015. _The Knowledge Capital of Nations_ . Cambridge: The MIT Press.

- Kane, Thomas J., Eric S. Taylor, John H. Tyler, Amy L. Wooten. 2011. Identifying Effective Classroom Practices Using Student Achievement Data.” _Journal of Human Resources_ 46(3): 587-613.

- Kane, T. J., and D. O. Staiger. 2008. “Estimating Teacher Impacts on Student Achievement: An Experimental Validation.” NBER Working Paper No. 14607.

- Kane, Thomas J., and D.O. Staiger. 2012. Gathering Feedback for Teaching: Combining High-Quality Observations with Student Surveys and Achievement Gains. Bill & Melinda Gates Foundation.

- Martin, Gayle and Waly Wane. 2016. “Education Service Delivery in Tanzania.” World Bank Report No. AUS5510. <u>https://microdata.worldbank.org/index.php/catalog/2748/download/39242</u>

- Metzler, J., and Woessmann, L. (2012). “The impact of teacher subject knowledge on student ”

- achievement: Evidence from within-teacher within-student variation _. Journal of Development Economics_ 99(2), 486–496.

- Molina, Ezequiel; Syeda Farwa Fatima, Andrew Y C Ho, Carolina Melo Hurtado, Tracy Wilichowksi, Adelle Pushparatnam. 2018. “Measuring Teaching Practices at Scale : Results from the Development and Validation of the _Teach_ Classroom Observation Tool.” World Bank Policy Research Working Paper No. 8653. Washington, D.C.

- Molina, Ezequiel , Syeda Farwa Fatima, Iva Trako, and Tracy Wilichowski.  2018b. “Teacher Practices in Mindanao: Results of the _Teach_ Classroom Observation Study.” Unpublished Manuscript. The World Bank.

- Molina, Ezequiel; Carolina Melo Hurtado, Adelle Pushparatnam, and Tracy Wilichowski. 2019. _Teach: Observer Manual_ . Washington, D.C.: World Bank

- Montenegro, Claudio E. and Harry A. Patrinos. 2014. “Comparable Estimates of Returns to Schooling Around the World.” World Bank Policy Research Working Paper No. 7020.

- Pianta, Robert C., Bridget K. Hamre, and Susan Mintz. (2012). _Classroom assessment scoring system: Upper Elementary Manual_ . Teachstone

- Rockoff, J.E., B. A. Jacob, T.s J. Kane, and D. O. Staiger. 2008. “Can You Recognize An Effective Teacher When You Recruit One?” NBER Working Paper 14485.

33
# Mangroves as Coastal Protection for Local Economic Activities from Hurricanes in the Caribbean<sup>1</sup>

_Juan Jose Miranda_<sup>_2_</sup> _Luigi Butron_<sup>_3_</sup> _Chrissie Pantoja_<sup>_4_</sup> _Rashmin Gunasekera_<sup>_5_</sup> _The World Bank_

## JEL Classifications: Q54, Q23, Q57

Keywords: Hurricanes, Natural disasters, Caribbean countries, Economic activity, Nightlight

> 1 Special thanks to Eric Strobl for sharing the windstorm hazard model results and helpful comments. The views expressed here do not necessarily reflect those of the World Bank or their member countries. All errors and opinions are our own.

> 2 Environment, Natural Resources and Blue Economy Global Practice: jjmiranda@worldbank.org.

> 3 Macroeconomics, Trade & Investment Global Practice: lbutroncalderon@worldbank.org.

> 4 Environment, Natural Resources and Blue Economy Global Practice: chrissiepantoja@gmail.com.

> 5 Social, Urban Rural and Resilience Global Practice: rgunasekera@worldbank.org.
We focus on four indicators of quality for health service delivery: (i) provider’s knowledge, (ii) availability of vaccines for children, (iii) availability of essential medicines; and (iv) availability of antenatal care (ANC) tests. All service delivery variables are calculated from the SDI data. Providers' knowledge is measured using the diagnostic accuracy rate aggregated at the facility level for general illnesses and diseases of children and pregnant women. Availability of vaccines is the percentage of vaccines available at the facility as a proportion of the seven essential vaccines asked in the survey: Rubella, Polio (OPV/IPV), Rotavirus, Pentavalent, Pneumonia, Bacille Calmette-Guerin (BCG), and Vitamin A. Likewise, availability of medicines is defined as the proportion of medicines available at the time of the survey in relation to the 19 essential medicines asked in the survey according to the standards of the World Health Organization (WHO). Finally, ANC tests refer to the number of the type tests available at the facility out of four test types (see Appendix for complete list of medicines and ANC tests).

Access to infrastructure is captured by five variables at the facility level: (i) access to electricity, differentiated by stable and unstable access,<sup>12</sup> (ii) the use of ICT for supply management, (iii) the availability of fridges for vaccine storage, (iv) distance to a primary or secondary road of at least fair quality<sup>13</sup> and (v) cellphone internet access (2G or 3G). Extensive description of the construction of the variables is provided in the Appendix. To refer to the specific questions used to construct the indicators, see Table A4.

Table 1 presents descriptive statistics for the main variables used, indicating the source of each of them (whether SDI or Geospatial information). A large fraction of health facilities is located in rural areas (76%), and over half of the sample are public institutions (63%). Most facilities have access to electricity (89%), mainly because of their connection to the national grid. Still, there is variation regarding the connection quality: only 55% of facilities have stable access to electricity. Likewise, most facilities have access to improved sources of water and sanitation as defined by the WHO,<sup>14</sup> and have basic medical equipment<sup>15</sup> (83%). There is variation in poverty levels, catchment areas, and access to other services (e.g., 2G/3G) for health facilities in Kenya.

> 12 Stable access to electricity: without interruptions in the last three months, or with interruption but access to a secondary source. Unstable access to electricity: with interruptions in the last three months and without access to a secondary source.

> 13 Several distance and quality variables were analyzed, and “Distance from a health facility to at least “fair” quality Primary Road (Trunk) or Secondary Road (Km)” was the best fitted variable.

> 14 <u>https://www.who.int/water_sanitation_health/monitoring/jmp04_2.pdf</u>

> 15 Basic equipment is a dummy variable indicating whether the facility has a thermometer, a stethoscope, a sphygmomanometer, and a weighing scale.

9
estimated _Mgt_ grows at the following rates between 2003 and 2007: -13 percent for grades K through 8, and 7 percent for grades 9 through 12. These rates line up with the Census growth rates for the corresponding age groups (equal to -13 percent and 13 percent , respectively) for the same period.

# **A.3 Household Characteristics**

We describe the estimation of the joint distribution of household location, child age and race, parental income and child poverty status for year 2000, and the adjustments made to this distribution for years 2003-2007. The term “demographic type” refers to a combination of race (black, white and Hispanic), income (16 values for income, each one representing the midpoint of the corresponding Census income bracket), and poverty status (eligible for free- or reduced-lunch, or not eligible). This yields a total of 96 demographic types. For each of the 13 grades and 433 locations (i.e., block groups) in our data, we estimate the number of children of each demographic type.

## **A.3.1 Household Types for Year 2000**

We do not observe the joint distribution of child age, race, household income and poverty status at the block group level. Instead, the Census provides us with the following information:

- tract-level joint distribution of age and race;

- tract-level joint distribution of age bracket, race and poverty status;<sup>38</sup>

- tract-level joint distribution of family income (by brackets) and race;

- block group-level joint distribution of age brackets and race.

We use this information to calculate the number of children in each demographic type, grade and location. Recall that Washington, D.C. includes 433 block groups and 188 Census tracts. The calculations described below apply to the 185 tracts (and the corresponding block groups) that include children aged 5-18. We proceed as follows:

1. Assuming the same distribution of age among the block groups of a given tract, we estimate the number of children of each age and race by block group.

2. For each block group, race and age we impute the poverty distribution that prevails for the corresponding tract, race and age. For each block group we obtain _narp_ , which is the number of children of each age _a_ , race _r_ and poverty status _p_ .

3. The tract-level income distribution for families is not adjusted by family size and hence does not reflect income per child. In the absence of data on the joint distribution of family income and size, we calculate tract-level average family income and average family size by race, and construct the city-level joint distribution of average family income and size. Then we reweight the original tract-level family

> 38Per federal guidelines, in order to qualify for free (reduced) lunch, a child must live in a household whose income is below 130 (185) percent of the Federal poverty guidelines for that household size. We pool children eligible either for free or reduced lunch into a single category. Thus, “poverty status” is a binary variable that describes whether the child is eligible for free- or reduced-lunch or not.

46
<!-- Start of picture text --> Hd itiyiessrrnnnnh itiyiessrrnnnnh<br><!-- End of picture text --> <!-- Start of picture text --> IL | HEHE<br><!-- End of picture text -->

Hd itiyiessrrnnnnh itiyiessrrnnnnh IL | HEHE Hiptehittren Hj paitiineiiig paitiineiiig

<!-- Start of picture text --> Hiptehittren<br><!-- End of picture text --> <!-- Start of picture text --> Hj paitiineiiig paitiineiiig<br><!-- End of picture text -->
|Table IV(b)|||
|---|---|---|
|Effect of Elections on Comm<br>[t-statistics in parenth|odity Ta<br>esis]|xesa|
||[13|[2]|
|Independent Variables:|||
|1 yr before Elections|1.10|2.47|
||[0.32]|[1.07]|
|2 yrs before Elections|2.03|2.64|
||[0.70]|[1.28]|
|3 yrs before Elections|4.20|6.89|
||[1.35]|[2.88]|
|4 yrs before Elections|3.86|4.09|
||[1.24]|[1.83]|
|State domestic product [SDP]||0.05|
|||[8.82]|
|Share of agriculture in SDP||-60.86|
|||[-3.22]|
|Proportion of rural population||-948.0|
|||[-10.4]|
|Number of Observations|<br>456|394|
|R-sq|0.87|0.94|

a. All taxes and SDP are in 1973 rupees per capita.

Regressions include year and state effects, and are reported with robust standard errors.

41
CONSUMPTION SUB-AGGREGATES 14 case; here suppose that there are only the two goods _c_ 1 and _c_ 2. Then from the consumer’s budget constraint it follows that _b_ ( _p_ ) = 1. □ This proposition establishes that a linear expenditure aggregate must be exclusively comprised of linear goods. The following corollary establishes that if _any_ good is linear in total expenditures, then _all_ goods must also be.

**Corollary 2.** _If the Engel curve of a good is a linear function of total expenditures x for all prices p, then the Engel curves of all goods are also linear functions of total expenditure._

_Proof._ Suppose that good 1 has linear Engel curves, with _p_ 1 _c_ 1( _p, x_ ) = _a_<sup>1</sup> ( _p_ ) + _b_<sup>1</sup> ( _p_ ) _x_ . Then from the budget constraint it follows that an aggregate of the remaining goods also have linear Engel curves, since � _ni_ =2<sup>_pici_=</sup><sup>_x −p_1</sup><sup>_c_1=</sup><sup>_−a_1(</sup><sup>_p_) + (1</sup><sup>_−b_1(</sup><sup>_p_))</sup><sup>_x_.ButthenbyProposition</sup> 3 all goods _i_ = 2 _, . . . , n_ also have linear Engel curves. □

# 4. Sub-aggregates and Poverty in Practice

Corollary 1 tells us that we can use any separable sub-aggregate provided expenditures on the sub-aggregate vary in proportion to total expenditures. But Proposition 3 and its corollary tell us that such a sub-aggregate will exist only under very special (and highly implausible) conditions. In particular, even if one can identify goods with linear Engel curves given prevailing prices, under different prices this linearity may fail.

However, from a practical point of view it may be possible to identify goods with linear Engel curves in one period, and then we may get lucky: in a subsequent period perhaps relative prices will not have changed in such a way that linearity will be compromised, and our sub-aggregate will work for measuring poverty.

4.1. **Data.** In the remainder of this paper we test our luck using data from expenditure surveys in Rwanda, Uganda, and Tanzania.<sup>5</sup> All surveys are large multipurpose household consumption surveys, representative at both national and urban/rural levels with large sample sizes. In Uganda about 3000 households were interviewed each year, in Tanzania between 3000 and 4000 each year, and in Rwanda the number of households increased substantially from 6900 to 14300 household observations per year.

> 5. Rwanda: Enquete Int ́egrale sur les Conditions de Vie des m ́enages de Rwanda (EICV1) 2001 and (EICV2) 2006 . Uganda: Uganda National Household Survey (UNHS) 2005/06 and 2009/10. Tanzania: Tanzania National Panel Survey (NPS) 2008/09 and 2010/11.
Table 9. Pension fund annual balance (RMB 10 billion)

|Experiment|1995|2000|2005|2010|2015|2020|
|---|---|---|---|---|---|---|
|Base|0|0.61|-2.55|-6.82|-11.48|-17.01|
|Exp. 3.1|0|0.61|3.05|2.82|2.77|2.72|
|Exp. 3.2|0|0.61|3.05|2.91|3.71|4.62|
|Exp. 3.3|0|0.61|3.05|2.91|3.71|5.03|
|Exp. 4.1|0|0.50|2.54|2.77|2.66|2.81|
|Exp. 4.2|0|0.50|2.54|2.87|3.79|5.01|
|Exp. 4.3|0|0.50|2.54|2.87|3.79|5.53|
|Exp. 5.1|0|0.50|1.18|1.63|0.84|0.26|
|Exp. 5.2|0|0.50|1.18|1.77|2.43|3.61|
|Exp. 5.3|0|0.50|1.18|1.77|2.43|4.36|
||2025|2030|2035|2040|2045|2050|
|Base|-20.00|-23.19|-27.06|-33.96|-43.48|-55.33|
|Exp. 3.1|4.59|7.22|10.29|12.47|13.89|14.58|
|Exp. 3.2|7.20|10.38|14.58|18.41|21.06|22.64|
|Exp. 3.3|11.60|18.71|26.93|36.28|45.88|54.28|
|Exp. 4.1|4.78|7.70|10.80|12.14|12.14|10.98|
|Exp. 4.2|7.68|11.09|15.41|18.52|19.50|18.73|
|Exp. 4.3|13.31|21.34|30.09|39.39|47.85|53.53|
|Exp. 5.1|0.96|3.22|5.60|4.98|2.51|-1.21|
|Exp. 5.2|5.62|9.03|13.81|16.58|16.31|13.89|
|Exp. 5.3|14.06|24.96|37.40|50.93|63.51|72.36|

Source: The authors.

The results of our simulations (Table 9) show that following the proposed reform, the sustainability of the pension system is guaranteed. Pillar 1 remains in surplus without raising the retirement age (experiment3.1, 4.1 and 5.1), and the annual surplus increases when coverage is expanded or the retirement age is raised. Because the government will be responsible for only pillar 1 - a smaller social pooling PAYGO system with only 20 percent replacement - the financial burden to the pension system will be reduced significantly.

The balanced contribution rates for simulation set 3 are listed in Table 10 together with the balanced contribution rate from the first two sets of simulations for comparison. This shows that if the current PAYGO system remains unchanged, the balanced contribution rate will be as high as 25 to 35 percent of payroll, even after expanding coverage or raising the retirement age. In experiment 2.5, after structural reform and tax-based financing of the transition cost, the balanced contribution rate for pillar 1 remained rather low, ranging from 9 to 12.5 percent between 2005 and 2050. This implies that with an 11 to 12 percent payroll tax, the small PAYGO pillar 1 (20 percent benefit) can be financially balanced. In experiment 3.1, 4.1, and 5. 1, the balanced contribution rates remained in the 10 percent to 12 percent range, largely consistent with our assumptions for the social security tax rate.

In the short or middle term (20 years), the new system will push _GDP growth_ up whether coverage is expanded to rural areas or not. For example, GDP is 1.2 percent higher than the baseline in set 3, and 0.8 percent higher in set 4, in 2020 (Figure 10). It seems better to raise the retirement age if coverage is expanded to rural areas. If pension coverage is restricted to urban areas then the best option is to set the retirement age for female workers at 60. No others increases in the retirement age are 31
Figure 5 shows local linear regressions of business practices on years of schooling for the combined sample, and country by country. We see a strong positive association. The association is weakest in Nigeria, where most of the sample has post-secondary education. Column 1 of Table 6 examines this association in more detail, also controlling for owner gender, age, country, and business sector, and for two other measures of human capital: digitspan recall and Raven test score. These latter two measures are both measures of analytical and cognitive ability, and were only collected in some countries (see Table 1). We see that business practices tend to be better in maleowned firms, and do not vary with the age of the owner. Conditional on this, each year of education is associated with a 1.2 percentage point increase in business practices: so a one standard deviation increase in education is associated with a 0.20 s.d. increase in business practices. Digitspan recall and Raven test both have positive and significant associations as well, but the magnitudes are smaller: a 1 s.d. increase in either measure is associated with a 0.03-0.08 increase in business practice scores, conditional on the other variables in this regression. Formal education therefore appears to have a stronger association than innate ability in explaining the practices used.

# **4.2 Family History of Entrepreneurship**

Evidence from higher-income countries indicates that children of self-employed parents are more likely to become self-employed themselves. Dunn and Holtz-Eakin (2000) find that the effect parents’ self-employment on occupational choice remains large even after controlling for wealth levels, suggesting that the transition of human capital from parent to child is the most important channel for the intergenerational effect. Motivated by this, in column 2 of Table 2 we examine whether owners whose parents were business owners have different business practices. We see that both having a father who owned a business and having a mother who owned a business are positively associated with business practices. This provides support for the idea that parents who are business owners transmit human capital relevant to running a business to their children.

# **4.3 Product Market Competition**

Bloom and Van Reenen (2007) emphasize the role of product market competition in driving out poorly managed firms or getting them to improve. We have three different measures of competition in our surveys. The first is the number of competitors the firm faces in their local area (typically in their marketplace or census tract). Many firms face a lot of competition, with the median facing 17
WPS3798

# **What Have We Learned From a Decade of Manufacturing**

**Enterprise Surveys in Africa?** *

Arne Bigsten** and Måns Söderbom***

## **Abstract**

In the early 1990s the World Bank launched the Regional Program on Enterprise Development in several African countries, a key component of which was the collection of manufacturing firm-level data. In this paper we review the research based on the data sets generated by these and subsequent firm surveys in Africa, with a special view to what we think are the most important policy implications. We survey the research on the African business environment, focusing on market size, risk, access to credit, labor, and infrastructure. We cover the research on how firms choose to organize themselves and how firms do business. We review the research on firm performance, including firm growth, investment and technology acquisition, and exports. We conclude with an extended discussion of the policy lessons.

## **JEL Classification** : D21, O12, O14.

**Key words** : African manufacturing, economic performance, survey data.

World Bank Policy Research Working Paper 3798, December 2005 _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._

* We thank the World Bank for funding this research. Many thanks go especially to John Nasir for encouraging us to write this paper and for excellent comments on an earlier draft. We are also grateful to participants at a seminar at Göteborg University for helpful comments and suggestions.

** Arne Bigsten is Professor of Economics at the Department of Economics, University of Göteborg, Göteborg, Sweden. He can be contacted by e-mail: Arne.Bigsten@economics.gu.se

*** Måns Söderbom is a Research Officer at the Centre for the Study of African Economies, Department of Economics, University of Oxford, UK. He can be contacted by e-mail: Mans.Soderbom@economics.ox.ac.uk
Ohlin, Bertil. 1933. Interregional and International Trade. Cambridge, Mass.: Harvard University Press.

Papageorgiou, Chris, and Antonio Spilimbergo. 2008. “Learning Abroad and Technology Adoption.” Unpublished Working Paper.

Romalis, John. 2004. “Factor Proportions and the Structure of Commodity Trade.” American Economic Review 94 (1): 67-97.

Scherer, Frederic. “Using Linked Patent Data and R&D Data to Measure Technology Flows.” In Z. Griliches, ed., R & D, Patents and Productivity. Chicago, IL: University of Chicago Press.

Schott, Peter. 2003. “One Size Fits All? Heckscher-Ohlin Specialization in Global Production.” American Economic Review 93 (2): 686-708.

Schott, Peter. 2004. “Across-Product versus Within-Product Specialization in International Trade.” Quarterly Journal of Economics 119 (2): 647-678.

Silverman, Brian. 1999. “Technological Resources and the Direction of Corporate Diversification: Toward an Integration of the Resource-Based View and Transaction Cost Economics.” Management Science 45 (8): 1109-1124.

Trefler, Daniel. 1993. “International Factor Price Differences: Leontief was Right!” Journal of Political Economy 101 (6): 961-987.

Trefler, Daniel. 1995. “The Case of the Missing Trade and Other Mysteries.” American Economic Review 85 (5): 1029-1046.

Vanek, Jaroslav.1968. “The Factor Proportions Theory: The N-Factor Case.” Kyklos 21 (4): 749-756.

Ventura, Jaume. 1997. “Growth and Interdependence.” Quarterly Journal of Economics 112 (1): 57-84.

Waugh, Michael. 2010. “International Trade and Income Differences.” American Economic Review 100 (5): 2093-2124.

Young, Alwyn. 1992. “A Tale of Two Cities: Factor Accumulation and Technical Change in Hong Kong and Singapore.” National Bureau of Economic Research Macroeconomics Annual 7: 13-63.

Young, Alwyn. 1995. “The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth Experience.” Quarterly Journal of Economics 110 (3): 641-680.

18