rafmacalaba/datause-encoder-data huggingface dataset:
ii

# ANNEXES

- A. Letter of Sector Policy

- B. Rural and Urban Sector Investments

- C. Project Costs

- D. Environmental Assessment Summary

- E. Cost-Benefit Analysis Tables

- F. Consultants' Services and Technical Assistance

- G. Estimated Schedule of Disbursements

- H. IDA Supervision Plan and Staff Input

- I. Key Performance Indicators

- J. Project File Documents

# **MAP** - **IBRD** NO. 26878

This report is based on the findings of a mission which visited Tanzania in May 1995. The mission was led by Mr. Gerhard Tschannerl (Sr. Municipal Engineer), and included Mr. Iraj Talai (Financial Analyst), Ms. Keta Ruiz (Economist), Ms. Rowena Martinez (Operations Analyst), Mr. Chris Banes (Municipal Infrastructure Specialist), Mr. Solomon Alemu (Civil Engineer), and Mr. Joseph Gadek (Sr. Sanitary Engineer). The peer reviewers were Ms. Letitia Obeng (AFTES), Mrs. Sandra Cointreau-Levine (Consultant), Mr. Robert Tillman (AFTES), Mr. Patrick Bultynck (AFTES) and Ms. Rita Hilton (ESDVP). Mmes. Janine Littleford and Nina Jones provided secretarial support. Messrs. Stephen Weissman and James Adams are the Division Chief and Director, respectively, for the operation.
Policy Research Working Paper 8592

# **Abstract**

This paper presents the most expansive and current cross-country dataset on education quality. The database includes 164 countries/territories covering over 98 percent of the global population from 2000–2017. Of these countries, 111 are low or middleincome economies. Harmonized learning outcomes are produced using a conversion factor to compare international and regional standardized achievement tests. These tests include PISA, TIMSS, PIRLS, SACMEQ, LLECE and PASEC. In addition, this paper includes the Early Grade Reading Assessment (EGRA) for the first time. This enables extension of the database substantially. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries that are often excluded from large international assessments. The database includes mean scores and disaggregates the data by subject, level, and gender. This paper further presents a series of methodological improvements including measures of uncertainty, fixed conversion factors for greater comparability over time and year-by-year data.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2019: The Changing Nature of Work_ —is a product of the Education Global Practice. 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://www.worldbank.org/research. The authors may be contacted at hpatrinos@worldbank.org.

_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
system (that is, when government interference is still common), patronage pressures may increase.

Turnover has been considerable, however, when one takes into account the fact that a total of 647 employees were dismissed or terminated their employment with the agency between 1993 and 2001 (of these 257 were dismissed, mostly on account of corruption). The highest turnover rates have occurred in the IT, accounting, and auditing areas, in which URA is paying considerably less than market rates.

In 1991 the fledgling URA increased salaries in order to attract private sector professionals and to motivate their ex-civil servants to improve job performance. Compared with the existing civil service salary structure, remuneration increased severalfold. The initial increase in salaries, however, has not kept place with inflation. Between 1991 and 2001 only one further salary increase took place. In 1998 the URA sought approval from the MOF for a revision of its salary scales. The MOF approved, but declined to provide any additional funding. As a result remuneration (salaries plus benefits) for high-level technicians has declined by approximately one-third since the mid 1990s.<sup>29</sup> Autonomy has also mattered in another important way in the case of URA: expatriates can be hired easily as staff, unlike in the public service. This is not a trivial point, as expatriates have come to play an important role in the development of the URA (the current CG is a European expatriate), serving as a substitute for an institutional bulwark against patronage pressures.

The Peruvian, Kenyan, and South African cases show the greatest commitment to human resource reform. The case of SUNAT is clearly the most far-reaching. SUNAT instituted a radical retrenchment program, increased salaries dramatically, and established a well-regarded in-house training institute. In Kenya there was a quasi-voluntary retrenchment, carried out in the context of declining staff numbers, and a selective increase in salaries. The KRA also provides training programs (out-sourced) for its staff. Survey and anecdotal information indicates that corruption decreased in both SUNAT and KRA as a result of the reforms. SARS did not undertake a retrenchment program,

> 29 “Medium Term Revenue Framework: Comparative Analysis of URA Salaries in 1994/95 and 2000/01,” URA.

21
# **2 Theoretical Model**

A crucial contribution of this study is incorporating the importing decision to a firm’s maximization problem. In a recent study, Halpern et al. (2005) analyze two channels by which imported products lead to productivity improvements in Hungary: their higher quality and imperfect substitution of foreign and domestic inputs. They show that two third of productivity increase caused by importing is attributable to an increase in the variety of intermediates used and the rest is due to an increase in quality. In another study, Amiti and Konings (2007), using data from Indonesia show that reducing input tariffs increase productivity three times more than a reduction in output tariffs. Both studies motivate for investigation of how importing relates to innovation and growth.

In the model, firms choose whether to import intermediate products and export any of their output facing fixed sunk costs for both markets. The sole factor determining firms’ participation in international markets is their efficiency levels which is exogenously assigned to them. Then I incorporate these static trading decisions with a dynamic framework of firm evolution. The dynamic model of evolution follows from Klette and Kortum (2004). Firms invest in R&D and these investments result in innovation of new products in a stochastic fashion.

I assume that there are _N+1_ identical countries and in each country there are two sectors formed of final good producers and intermediate goods producers. In each country, a composite good _Y_ is produced by a large group of monopolistically competitive final goods producers. Each firm produces multiple products (each of which is a different variety). Consumption of the composite good _Y_ is determined by a CES production function given in equation 1 as where _j_ is an index over varieties chosen from a set _J_ and σ >1 is the elasticity of substitution between different varieties. Producers are distinguished only by their efficiency levels, indexed by φ >0. The solution of this model follows from Melitz (2003). Under monopolistic competition, producers with same efficiency levels charge the same price and make the same profit for each product they produce. The static profit maximization problem for production of any product, for given wage rate _w_ , yields revenue and profit given as where p(φ) is the price charged by the firm, P is aggregate price index and E is aggregate expenditure of the composite good (E=PY).

5
**<u>Table 8: Labor Costs and Employment-intensive Growth</u>**

||Manufacturing<br>Employment-intensive<br>Growth|Secondary Sec<br>Employment-i<br>Growth|tor<br>ntensive|Agricultural P<br>intensive Gro|roductivity-<br>wth|
|---|---|---|---|---|---|
|Monthly Minimum<br>Wage|-<br>0.000177***<br>(-3.22)|-<br>0.000295***<br>(-3.02)||0.000129***<br>(3.92)||
|Minimum Wage<br>Relative to Average|-0.0497**<br>(-2.31)|||||
|Paid Annual Leave|||||0.000552**<br>(2.89)|
|Severance Payment|||0.0103**<br>(3.08)|||
|Constant|0.0198**<br>0.0178**|0.0310**|-0.0380**|-0.0215***|-0.0165**|
||(2.83)<br>(2.84)|(2.50)|(-3.46)|(-4.48)|(-2.84)|
|Observations|42<br>31|42|17|42|15|
|AdjustedR-squared|0.201<br>0.155|0.158|0.059|0.182|0.099|
|*p<.10,**p<.05,***|p<.01.T-statisticsinparenthe|ses.||||

Table 8 shows the results of those variables that were significantly correlated with the sectoral employment and productivity profile of growth.<sup>23</sup> The results suggest that employment-intensive growth in manufacturing is affected by minimum wages (both in absolute terms and relative to the average manufacturing wage) having a negative effect. Minimum wages also reduce secondary sector employment-intensive growth, while severance appears to have a positive effect. This last result is somewhat surprising. A possible explanation is that it may raise the supply of labor to this sector, or may reduce turnover during recessions. No other regulation variables considered had any explanatory power. Labor costs also appear to affect productivity-intensive growth in agriculture, although the signs of the coefficients are not straightforward to interpret. Minimum wages are significant in explaining agricultural productivity-intensive growth with a positive coefficient. A possible interpretation is that minimum wages and paid annual leave are binding mostly for non-agricultural firms, and thus higher minimum wages and better employment benefits make employment in the urban sector more attractive, so that workers are more willing to queue for an urban good job. This outflow from agriculture (into unemployment and bad jobs) raises output per worker in agriculture.

None of the unionization variables appear significant in explaining employment-intensive growth in manufacturing or the secondary sector. Instead, agricultural productivity growth is affected positively by strikes and lockouts, the number of workers participating

> 23 Non-significant results are not reported for brevity.

25
calculated from the census, or obtained from ancillary data sources. Some of these are then included in the model specification to capture small area heterogeneity.<sup>7</sup> To probe the method’s success in this respect we use census data to estimate two sets of models. The first set comprises a single state-level model (including a number of municipality level aggregates as regressors). The second comprises a set of 853 municipality-specific models. We compute the average predicted value of income for each municipality on the basis of the two sets of models. Figure 11 shows that municipality-level predictions are closely centered on the main diagonal indicating a close match of the pairs. In other words, predicted income is not markedly different if the model is estimated with our state-level model as opposed to a municipality-specific model. The estimated correlation among the two predictions is 0.90, and 80% of the conditional means based on the municipality-specific model are found within the 95% confidence interval estimated using the state-level model. It seems that specifying a model at the level of region _R_ is not particularly problematic for estimating welfare at area _A_ as long as the model captures local heterogeneity.<sup>8</sup>

> 7 Enumeration area, district and municipality level variables, such as total population, formal sector employment shares, literacy rates, availability of publicly provided water and  sanitation services, and so on, are generally found to be strongly correlated with household per capita income, even after controlling for household level characteristics.  To assess how well our strategy of using such variables works we can estimate  models for of our set of samples using first a fixed effects specification and then using local-level averages instead.  We observe a ratio of the two R-squares in the range of of 0.95-1.00, confirming that the latter model performs nearly as well as a fixed-effects specification (see also Demombynes et al , 2006).  For further detail on the models estimated, see further below and also Appendix 1. 8 By removing all municipal-level averages from the state-level model, the correlation in the sample decreases to 0.50 indicating that small areas heterogeneity control must be taken into account during model specification. This heterogeneity affects the precision of estimations and can also lead to an overestimation of the error component.

15
5 seroprevalence) and perceptions on ART, and thus lead to biased estimates of disinhibition behaviors. This source of bias remains a concern with the results of Cohen et al. (2009) despite the careful design of their field survey<sup>6</sup> .

Controlling for unobserved individual characteristics, we find suggestive evidence of disinhibition behaviors: individuals who are more aware of ART engage more in risky sex. Moreover, men and women respond differently to the perceived changes in risk. In particular, risky behaviors increase for men who believe, wrongly, that AIDS can be cured, while risky behaviors increase for women who believe, correctly, that ART can control AIDS but cannot cure it. These findings have two main implications. First, our results suggest that prevention programs that provide the correct information on ART may reduce disinhibition behaviors for men, although given the small and declining percentage of individuals believing wrongly that AIDS can be cured (see table 1, 8% in 2007 and 6% in 2008) the impact of such a program is bound to be limited. Prevention messages for women, however, need to go beyond the correct information on ART and stress the importance of safe sexual behaviors. The second implication is that to effectively control AIDS, prevention programs should not be traded away in order to scale up access to treatment.

The remainder of the paper is organized as follows. We begin by providing a conceptual framework to motivate our empirical results in section 2. Next, we discuss our survey, and show a summary of the main variables used in the analysis in section 3. We discuss our empirical strategy in section 4, and report our estimation results in sections 5. Section 6 concludes.

> 6Cohen et al. (2009) conduct their field survey in two steps. First, they administer a survey including questions on sexual behaviors and perceptions on ART. The second step consists of HIV testing of respondents. Note that individuals who knew their sero-prevalence were excluded from the sample. The approach prevents respondent to associate their answers to the HIV test, and therefore adjust the formers. However, it does not solve the issue associated with unobservable individual characteristics.
Duflo, Esther and Emmanuel Saez, 2003, The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence From a Randomized Experiment, _Quarterly Journal of Economics_ , Vol. 118, No. 3, Pages 815-842.

Edlund, Lena and Rohini Pande, 2002, ̳Why have Women Become Left-Wing? The Political Gender Gap and the Decline in Marriage‘, _Quarterly Journal of Economics_ , Vol. 117, No. 3, Pages 917-961.

Fafchamps, M. and F. Gubert, 2006, ―The formation of risk sharing networks‖, _Journal of Development Economics_ , 83(2):326-350.

Fernandez, Raquel. 2007. Women, work and culture. _Journal of the European Economic Association_ , 5(2–3):305–332.

Fernandez, Raquel and Alessandra Fogli. 2009. ―Culture: An empirical investigation of beliefs, work, and fertility.‖ _American Economic Journal: Macroeconomics_ 1(1):146–177.

Fortin, Nicole. 2005. ―Gender role attitudes and the labour market outcomes of women across oecd countries.‖ _Oxford Review of Economic Policy_ 21:416–438.

Funk, Patricia. 2010. ― Social Incentives and Voter Turnout: Evidence from the Swiss Mail Ballot System ‖, _Journal of the European Economic Association_ 8(5): 1077–1103.

Hoddinott, John and Haddad, Lawrence, 1995, Does female income share influence household expenditures? Evidence from Cote D‘Ivoire, Oxford Bulletin of Economics and Statistics, vol. 57(1), pp. 77–95.

Gerber, Alan S., and Donald P. Green, 2000a, ―The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment.‖ _American Political Science Review_ 94(3): 653–63.

Gerber, Alan S., and Donald P. Green. 2000b. ―The Effect of a Nonpartisan Get-Out-the-Vote Drive: An Experimental Study of Leafletting.‖ _Journal of Politics_ 62(3):846-857.

Gerber, Alan S., and Donald P. Green. 2001. ―Do Phone Calls Increase Voter Turnout?: A Field Experiment.‖ _Public Opinion Quarterly_ 65(1):75-85.

Gerber, Alan S., and Donald P. Green. 2004. ―Get Out the Vote: How to Increase Voter Turnout‖ Brookings Institution Press: Washington, DC.

Gerber, Alan S., and Donald P. Green. 2005. ―Do Phone Calls Increase Voter Turnout? An Update.‖ _Annals of the American Academy of Political and Social Science_ 601: 142-154.

26
7 groups. We focus on the employment share of urban tradables (MFG & FIRE). We could have alternatively focused on urban non-tradables but it is not yet clear which sectors should be included. Having obtained employment data for each administrative unit (1960-2015), the next step is to use these data to obtain the labor shares for each FUA.

Censuses only take place every 10-15 years. To ensure that we have enough countries for our comparison, we select for each country-FUA the closest observation to the year 2000 (within the 1990-2015 period). Doing so, we are left with 6,865 FUAs in 74 countries. Lastly, we need data on their population size c. 2000. The GHSL database reports their population for the “epoch 2015”. However, population is almost always estimated for earlier years. In many cases, the last census indeed took place in the 2000s.

**Sampling.** The 6,865 FUAs include 3 billion people and account for 75% of world’s urban population. Kolmogorov–Smirnov tests confirm that the distribution of city sizes is not significantly different between our sample and the world. However, data on a few large countries are not available in IPUMS To increase our developed (e.g., Japan). sample’s representativeness, we divide all the countries in the world into ten deciles based on their log per capita GDP c. 2000 (PPP; source: World Bank (2021)). We then obtain the share of each decile in the world’s urban population and compare these shares to the shares in our sample. Based on the differences, we create weights over-sampling developed countries.

**Methodology.** Since we aim to identify which cities have high shares of MFG+FIRE relative to other cities of the same size and for a given level of urban economic development, we categorize the FUAs in 10 deciles. Based on a range of log population from 10.8 (50K) to 17.2 (30 million), the resulting thresholds are: 95K, 180K, 341K, 648K, 1,227K, 2,306K, 4.377K, 8,351K, and 15,570K. The top two bins end up having few FUAs (N = 22 and 13, respectively), so we aggregate them together (N = 35).

Then, using the sample of 6,865 FUAs, we run a regression relating the FUA’s employment share in MFG+FIRE (%) c. 2000 to 8 size category (CAT) dummies (omitting the lowest size category, hence 50-95K) and their interactions with the 2000 urban share (URB) of the FUA’s country. We also add a dummy if the FUA is the capital city of the country in 2000 (CAP). For FUA _a_ in country _c_ and population category _p_ , the model is: MFGFIRE _a,c,_ 2000 = _α_ +Σ<sup>9</sup> _p_ =2<sup>_βp_1(CAT</sup><sup>_a_=</sup><sup>_p_)+Σ9</sup> _p_ =2<sup>_γp_1(CAT</sup><sup>_a_=</sup><sup>_p_)</sup><sup>_∗_URB</sup><sup>_c_+</sup><sup>_δ_URB</sup><sup>_c_+</sup><sup>_ζ_CAP</sup><sup>_a_+</sup><sup>_μa_</sup> estimates of sectoral shares. All FUAs in the same first-level unit are then assigned the same composition.
almost 15 percent (equivalent to 1.9 USD billion) of total exports, on average. This is a striking number for one firm. The number two ranked firm holds a share of 6 percent on average, still a large share for one firm, but less than half as much as the number one firm.<sup>9</sup> In sum, we find that a small number of very large firms co-exist with a large number of smaller firms. This distribution is almost identical regardless of the country or year we consider.

# **3. Superstars and Trade Patterns**

The evidence above highlights the extremely skewed distribution of exporters in all countries. In this Section, we examine how large firms influence trade patterns. We look at three things: (i) the similarity in distributions of total values exported and the number of firms by product groups across country pairs; (ii) average firm size (intensive margin) versus firm count (extensive margin) as determinants of sectoral export shares, and superstars’ contribution to average size; and (iii) the similarity in the relative size of non-superstar firms across countries or sectors.

# _Bilateral Correlations_

We begin by looking at simple correlations to compare how countries export, using available data from 2006 to 2008 averaged across country-product groups (at HS 2-digits). Averaged data has the advantage of reducing potential noise in the data from a particular year. As we mentioned above, we use this period because all of the countries in the sample have data around this time period. This creates a cross-section of data on total values exported, average firm size and firm number by country-product group, averaged for this period.

> 9 We also made this calculation for a sample considering only manufacturing products and the results are very similar.

9