rafmacalaba/datause-encoder-data huggingface dataset:
where _V_<sup>ˆ</sup> is evaluated at estimates of the variance or covariance components (Moulton 1986). In sum, we estimate the effect of school and student-level factors on test scores using equation (1.3), with coefficients estimated according to equation (2.3), and standard errors estimated according to equation (2.4).

# <u>Data</u>

Data used are from three sources: for Peru from the 1997 First Comparative International Study on Language, Mathematics and Associated Factors (“Regional Laboratorio”), overseen by The Regional Education Office for Latin America and the Caribbean (OREALC), a division of the United Nations Economic and Social Council (UNESCO); from Guatemala’s national Laboratorio exam, which was based on the UNESCO test; and from Mexico’s National Standards exam. For the present analysis, each exam score was standardized to have a mean of zero and a standard deviation of one.

The regional Laboratorio study applied the same 32 multiple choice math questions and 19 multiple-choice Spanish language questions to students from 13 countries in 1997. The data were not scaled with item response theory (IRT), so one correct answer on any question translated to one point on a student’s final score. The sample was stratified to ensure sufficient observations of public, private, urban, rural and metropolitan pupils in each country. Data were collected for 100 schools in each country, with 40 children per school surveyed. Half of the students were in the third and half in fourth grade. The survey also gave questionnaires to school principals, teachers, parents or legal guardians of tested pupils and the students themselves. The test and 10
The main data sources are the World Bank Enterprise Surveys (ES) in 709 cities of 128 countries. The ES data are collected by the World Bank to benchmark the business climate in (mostly) developing countries and to understand the determinants of firm performance. The annual GDP per capita of sample countries ranges from around US$200 (low-income countries) to US$46,000 (upper-middle-income countries). In each country the survey is based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the nonagricultural private economy in the country.<sup>2</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade.<sup>3</sup> Each survey is largely a cross-sectional data set. However, firms are asked about their sales and employment for both the survey year and three years prior to it, which allows us to construct annualized growth rates.

We include data collected after 2006, although some Enterprise Surveys were conducted earlier. Before 2006, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology, and the samples were not generally representative. A complete list of variables and data sources is shown in table 1 and table 2; the summary statistics for our key variables are presented in table 3. Our final sample consists of approximately 80,000 firms covering 709 cities in 128 countries.<sup>4</sup>

# **Measurements of the business environment**

We first discuss the logic of the _Doing Business_ (DB) and the Global Competitiveness (GC) data collection and the pros and cons of these data sets. The World Bank _Doing Business_ project provides objective measures of business regulations for local firms in 190 economies. Each indicator in _Doing Business_ is calculated from several objective questions based on case scenarios with specific assumptions. Instead of surveying firms, the _Doing Business_ project collects the data from legal professionals, such as lawyers, judges, public officials, and other professionals (that is, accountants, architects, and engineers). In addition, about two-thirds of the data are based on a reading of the law because the primary goal of the project is to measure what is regulated by the law of a certain country.

> 2 Thus wholly state-owned firms are not included in the sample.

> 3 For a more detailed description of the WBES, see http:\\www.enterprisesurveys.org.

> 4 For each dependent variable, the number of observations differs. The figure of 80,000 is for the dependent variable of employment growth, which has fewer missing observations. The 709 cities in 128 countries refers to the sample where the issue of missing data does not arise.

7
-14-

# Table 1:

|:?FECTIV' COSTOFBORROWIN-<br>GTRY<br>197|0.2966(|S)|||||
|---|---|---|---|---|---|---|
||1970|1972|1972|1973|1974|1975|
|:nterest<br>Rate Nominal<br>(A)|10|10|10|12|12.5|14|
|<br> <br> <br>olission<br>and Taxes (3)|<br>5.1|<br> <br>5.1|<br>5.1|5.1|3.1|5.1|
|<br>lorroving<br>aste Nominal<br>tC)|15.1|15.1|15.1|17.1|17.6|19.1|
|<br> <br> <br>lorrowing<br>Rate Nomisal<br>Compounded (D)|15.96|15.96|15.98|18.23|18.80|20.51|
|<br> <br> <br>ompensating<br>Balances<br>(1)|<br>0.36|<br>0.35|<br>0.43|0.35|0.39|0.36|
|<br> <br> <br>:nterest<br>Rate on CD"s(1)|<br>0|<br> <br>0|<br>0|0|0|0|
|:ffective<br>Cost<br>of Loans to &vrrower|||||||
|rominal((C-(E.F))/(1-E))|23.59|23.23|26.49|26.31|28.85|29.84|
|<br>fominal Compounded<br>(D.(E.F)))I(1-E))|24.96|24.58|26.03|28.04|30.81|32.05|

||1976|1977|1978|1979|1980|1981|
|---|---|---|---|---|---|---|
|:nterest<br>Rate<br>Nominal<br>(A)|14|14|15.5|16.58|25.67<br>|33|
|:omussion andTaxes<br>(B)|5.1|5.1|<br>5.1|5.1|10.1<br>|8.8<br>|
|lorrowing<br>Rate<br>Nominal<br>(C)|19.1|19.1|20.6|23.66|35.77<br>|41.8<br>|
|Sorrowing<br>Rate<br>Nominal Compounded (D)|20.51|20.51|22.25<br>|25.87<br>|40.86<br>|48.82<br>|
|:ompensating<br>Balances<br>(E)|0.33|0.37|0.35|0.46|0.52|0.49|
|Interest<br>Rate<br>on CD's (F)|0|0|0|0|0|0|
|_ffective<br>Cost<br>of Loans<br>to Borrower|||||||
|4ominal((C-(E.F))/(1-E))|28.51|30.32|31.69|43.85|74.52|81.96<br>|
|Yominal Compounded<br>((D-'E.F)))/(I.E))|30.62|32.56|34.23|47.90|85.13|95.73|

||1982|1983|1984|1985|1986|
|---|---|---|---|---|---|
|:nterest<br>Rate<br>Nominal<br>(A)|38<br>|38<br>|45.6<br>|52<br>|52<br>|
|:ommission<br>and Taxes<br>(B)|7.8|7.3|7.5<br>|6.5<br>|6.4<br>|
|3orroving<br>Rate<br>Nominal<br>(C)|45.8<br>|45.3<br>|53.1<br>|58.5<br>|58.4<br>|
|3orrowing<br>Rate<br>Nominal<br>Compounded (D)|54.28<br>|53.59<br>|64.64<br>|72.63<br>|72.48<br>|
|:ompensating<br>Balances<br>(E)|0.38|0.29<br>|0.32|0.33|0.3<br>|
|Interest<br>Rate on CD's (F)|0|<br>0|5.1|7.5|14.6|
|Effective<br>Cost<br>of Loans to Borrower||||||
|Nomjnal((C-(E.F))/(1-E))<br>|73.87<br>|63.80<br>|75.69<br>|83.62<br>|77.17<br>|
|Nominal Compounded ((D-(E.F)))I(l-E))|87.55|75.48|92.66|104.71|97.29|

SOURCE: Central Bank of Turkey. Capital Markets Board. Institute of Bankers. World Bank Staff Estimates
Table 1: Taxation of the Financial Sector

|At End-Year|1978|1979|<br>1980|1981|1982|1983|1984|1985|1986|1987|1988|Average|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|COTE d'IVOIRE|||||||||||||
|% of GDP<br>|3.0<br>|5.8<br>|<br>1.7<br>|<br>1.7<br>|<br>2.2<br>|<br>2.0<br>|<br>1.8<br>|<br>1.6|<br>0.9|<br>1.1|<br>1.1|2.1|
|% of M2<br>|9.7<br>|20.7<br>|6.7<br>|6.6<br>|8.6<br>|8.1<br>|6.5<br>|5.4<br>|3.1<br>|3.7<br>|3.7<br>|7.5<br>|
|% of Govt Revenue|N.A.|N.A.|6.9|7.0|8.8|7.4|6.1|5.8|4.1|5.5|5.6|6.4|
|GHANA|||||||||||||
|=z====|||||||||||||
|% ofGDP<br>|2.8<br>|12.1|10.9|1.6|13.1|0.6|1.4|2.4|2.8|1.9|1.7|4.7|
|X of M2<br>|14.9<br>|62.5<br>|72.9<br>|11.9<br>|91.1<br>|7.6<br>|13.2<br>|21.1<br>|24.8<br>|15.9<br>|13.3<br>|31.8<br>|
|X of Govt Revenue|N.A.|N.A.|190.9<br>|30.7<br>|236.5<br>|8.7<br>|15.1<br>|19.8<br>|19.9<br>|13.4|12.8|60.9|
||||...|3====|======|======|==2===<br>**=SSSSZ**|==S==<br>**S==33======**|===<br>**===S==3===**|**=SS...S=ta**|||
|KENYA|||||||||||||
|% ofGDP|1.3|3.9|2.9|3.1|1.0|0.9|0.8|0.9|0.8|1.0|0.9|1.6|
|% of M2<br>|3.8<br>|12.2<br>|10.3<br>|11.4<br>|3.3<br>|3.2<br>|2.8<br>|3.4<br>|2.9<br>|3.5<br>|3.4|5.5|
|%.of Govt Revenue|N.A.|N.A.|13.0|14.1|4.5|4.1|3.6|4.1|3.9|4.4|4.0|6.2|
|NIGERIA|||||||||||||
|%ofGDP<br>|1.4<br>|2.8<br>|4.0<br>|1.0|8.0<br>|7.5|0.7|2.5|1.0|8.6|6.3|4.0|
|%of M2<br>|7.1|13.4|15.0|3.9|<br>31.8|26.4|2.4|8.9|3.9|<br>40.o|29.2|16.6|
|Xof Govt Revenue|N.A.|N.A.|25.0|7.8|65.1|67.7|6.7|18.7|5.7|48.4|35.5|31.2|
|...<br>=<br>======z==|<br>**3**<br>**s=**<br>*****|**=3=3**<br>=|===|**=**<br>|**...==**<br>**3====**|==3=3==3==|=srss-re--====|=s======|||||
|ZAMBIA|||||||||||||
|%ofGDP<br>|2.3<br>|2.8<br>|3.3<br>|3.3<br>|<br>6.0|6.5|10.6|4.5|7.8|11.7|18.9|7.1|
|%of M2|8.9|9.5|12.0|11.9|18.4|20.7|40.1|22.4|33.5|40.8|62.1|25.5|
|%of Govt Revenue|N.A.|N.A.|13.6|14.1|25.4|.d7.9|47.8|19.7|33.4|58.4|111.4|39.1|

Note: Based on Measure 1.

38
**Table 1: Overview of types of recent surveys for the sub‐Sahara Africa Region**

|Country|survey name|date|type of survey|
|---|---|---|---|
|Botswana|BCWIS|2009|detailed|
|Cameroon|EEIS2|2010|detailed|
|Malawi|IHS|2010|detailed|
|Rwanda|EICV|2010|detailed|
|Uganda|UNPS|2010|detailed|
|Zambia|LCMS|2010|detailed|
|Niger|LSS|2011|detailed|
|Sierra leone|SLIHS|2011|detailed|
|Tanzania|HBS|2011|detailed|
|South Africa|GHS|2011|detailed|
|Mauritius|CMPHS|2012|detailed|
|Nigeria|GHS_2|2012|detailed|
|Swaziland|HIES|2009|short|
|The Gambia|IHS|2010|short|
|Lesotho|HBS|2010|short|
|Madagascar|EICVM|2010|short|
|Sao Tome and Principe|IOF|2010|short|
|Senegal|ESPS|2011|short|
|Togo|QUIBB|2011|short|
|Ethiopia|UEUS|2012|short|
|Ghana|LSS|2012|short|

28
22 ET]herb is a high level of evasion and delay, particularly in the countries which have high and sustained inflation (where delaying payment means a reduction in one's real contribution); ... the State - under pressure to meet multiple - and demanding requirements fails to comply with its financial obligations, thus leading to the accumulation of very large debts;...and...the transfers (or "loans") from the pension funds to cover the deficits of health programmes (the cost of which have escalated due to "a capital-intensive system of remedial medicine"] are difficult to reimburse in practice, which has contributed to the de-capitalisation of the funds, at a time it is necessary to use the accumulated technical reserves to meet the current pension payments.

Two additional factors which have had a notable effect on the financial solvency of social security in Uruguay are low retirement age (60 for men, 55 for women - but retirement at substantially younger ages for unemployed persons was permitted, and often accepted, until 1981), high life expectancy (72 at birth 2 but of course higher for those who survive until retirement age), and a high rate of emigration among the young. Together, these factors lead to a very high dependency ratio of .82 pensioners per contributor in 1983 (Mesa-Lago, 1985, cited in World Bank, 1986, pp. 80) and contributed to a deficit fully 60 percent as great as revenues in 1980 (Mesa-Lago 1986), requiring a subsidy from the central government of over four percent of GDP in 1983 (World Bank, 1986, p. 83). 3 4 It was partly Chile's experience with the need to use general revenues to maintain the solvency of its social security system that led it to institute its privatized system. A noteworthy feature of this system is that it maintains its actuarial soundness through full funding and the limitation
||Male|**A**<br>Age|**ppendix Table**<br>Single /<br>Divorced|**2. Orthogon**<br>Adults in<br>HH|**ality checks**<br>Completed a<br>universitydegree|Has active<br>credit|Has savings<br>account|Owns business /<br>Self employed|<br>HH monthly<br>salary (USD)|
|---|---|---|---|---|---|---|---|---|---|
|_Panel A: Savings auditors_||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|High experience|-0.168|-0.653|-0.040|-0.028|0.051|0.031|-0.185|0.110|190.285|
||(0.187)|(2.934)|(0.183)|(0.560)|(0.133)|(0.175)|(0.133)|(0.166)|(153.402)|
|Investment|0.116|-1.890|0.076|0.790|-0.308**|-0.084|0.018|-0.138|164.670|
||(0.188)|(2.984)|(0.185)|(0.566)|(0.139)|(0.172)|(0.133)|(0.170)|(142.295)|
|R-squared|0.036|0.037|0.026|0.135|0.323|0.054|0.340|0.145|0.166|
|N|33|33|33|33|33|33|33|33|33|
|**Means**||||||||||
|Peru|0.495|33.000|0.667|2.833|0.417|0.500|0.583|0.250|906.750|
|Mexico|0.513|31.000|0.500|4.000|0.500|0.250|0.500|0.000|862.400|
|Ghana|0.534|30.471|0.529|3.529|0.412|0.294|1.000|0.471|660.824|
|_Panel B: Credit auditors_||||||||||
|High experience|0.150|0.070|-0.155|-0.332|-0.152|0.087|0.093|0.310**|-94.287|
||(0.152)|(2.708)|(0.142)|(0.438)|(0.144)|(0.144)|(0.129)|(0.143)|(99.918)|
|High indebtedness|0.032|1.839|-0.027|-0.214|-0.030|0.276*|0.089|-0.128|43.535|
||(0.152)|(2.708)|(0.143)|(0.434)|(0.145)|(0.143)|(0.130)|(0.143)|(100.256)|
|R-squared|0.032|0.013|0.138|0.044|0.097|0.142|0.194|0.147|0.342|
|N|47|47|47|47|47|47|47|47|47|
|**Means**|0.484|32.596|0.532|2.745|0.447|0.468|0.702|0.426|651.721|
|Peru|0.493|31.917|0.750|3.083|0.583|0.667|0.667|0.333|1035.125|
|Mexico|0.536|32.611|0.333|2.778|0.278|0.444|0.500|0.389|554.027|
|Ghana<br>This table reports results of regressin<br>All regressions include country fixed|0.425<br>g each auditor chara<br>effects. Robust stan|33.059<br>cteristic agai<br>dard errors i|0.588<br>nst different tr<br>n parenthesis. L|2.471<br>eatment dum<br>evels of signi|0.529<br>mies. Panels A and B<br>ficance * p<0.10 **|0.353<br>reports resu<br>p<0.05 ***|0.941<br>lts for savings<br>p<0.01|0.529<br>and credit auditor|484.524<br>s, respectively.|
Damania, Richard, Sébastien Desbureaux, Marie Hyland, Asif Islam, Scott Moore, Aude-Sophie Rodella, Jason Russ, and Esha Zaveri (2017). “Uncharted Waters: The New Economics of Water Scarcity and Variability.” Washington, DC: World Bank. doi:10.1596/978-1-4648-1179-1.

Davis, Jennifer, Alice Kang, and Jeffrey Vincent (2001), “How Important Is Improved Water Infrastructure to Microenterprises? Evidence from Uganda.” _World Development_ 29(10): 175367.

Distefano, Tiziano, and Scott Kelly (2017). “Are we in deep water? Water scarcity and its limits to economic growth.” _Ecological Economics_ 142 (2017): 130-147.

Escribano, Alvaro, J. Luis Guasch, and Jorge Pena (2010), “Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa: Cross-Country Comparisons Base on Investment Climate Surveys from 1999 to 2005” World Bank Policy Research Working Paper 5191.

Fisher-Vanden, Karen, Erin T. Mansur, and Qiong (Juliana) Wang (2015), “Electricity shortages and firm productivity: Evidence from China’s Industrial Firms,” _Journal of Development Economics_ 114(2015): 172-188.

Gillanders, Robert (2014), “Corruption and Infrastructure at the Country and Regional Level,” _The Journal of Development Studies_ 50(6): 803-819.

Haltiwanger, John, Ron S. Jarmin, and Javier Miranda (2013), “Who Creates Jobs? Small versus Large versus Young,” _The Review of Economics and Statistics_ 95 (2): 347-361.

Harris, Richard and Catherine Robinson (2003), “Foreign Ownership and Productivity in the United Kingdom using the ARD.” Review of Industrial Organization 22(3): 207-223.

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# **6. Conclusion and policy implications**

This paper used a detailed plot level data set to investigate the impact of limited transfer rights and perceived land tenure insecurity on investments in coffee, chat and eucalyptus. We find strong evidence that the share of land allocated to coffee increases if transfer rights are present, while expectations of losing land in the next five years due to land reform reduces coffee planting. Eucalyptus and chat are also strongly responsive to transfer rights. We show that the institutions of property rights matter for efficiency, investment and growth, with clear evidence from one of the poorest countries in the world.

How significant is this effect for policy? A simple extrapolation suggests that the effects are substantial. On average in this period, only 59 percent of plots have a full transfer rights. Moving this to 100 percent, and using a possibly conservative estimate of the marginal impact, based on the instrumented fixed or random effects tobit model (0.3 15), this would suggest that about 10 percentage points more land would be cultivated with coffee – or an increase by more than a third on the 27 percent share of land allocated at present. The threat of expropriation (in the next five years) has substantial effects for those fearing that land will be taken away, although because only about 5 percent expressed this fear in 1999, the impact on average coffee holdings is only about 1 percentage points less land allocated to coffee. Overall, not many farmers appear to fear an immediate expropriation of their land, but it does not mean at all that they perceive to have secure transfer rights. Transfer right insecurity is a major drag on efficiency and growth, even if the threat of immediate expropriation is currently relatively low. A deficiency of the current land policy is that it does not succeed in offering transfer rights to farmers that would allow a more long-term planning orientation of farmers.

The increases implied by the coefficients on transfer rights for eucalyptus and chat are even (relatively) higher. Overall, the regression using the total share to coffee, eucalyptus and chat, the increase would be about 9 percentage points, or still an increase by more than a quarter. While this expansion of coffee, eucalyptus and chat would occur at the expense of other crops, the fact that this effect is directly linked to tenure insecurity and transfer rights suggests a major efficiency loss. In these farming systems, alternative crops are limited and much land is devoted to low return 24
# Figure 8. The Global Age Distribution by Income Classification

# A. All Cohorts

<!-- Start of picture text --> High income Upper−middle income<br>countries countries<br>600<br>400<br>200<br>0<br>Lower−middle income Low income<br>countries countries<br>600<br>400<br>200<br>0<br>0 10 20 30 40 50 60 70 80+ 0 10 20 30 40 50 60 70 80+<br>Age cohorts<br>Population (millions)<br><!-- End of picture text -->

# B. 60+ Cohorts

<!-- Start of picture text --> Age cohorts<br>400<br>80+<br>70−80<br>300<br>60−70<br>200<br>100<br>0<br>High Upper−middle Lower−middle Low<br>income income income income<br>countries countries countries countries<br>Population (millions)<br><!-- End of picture text --> Source: World Population Prospects.

16
- [29] Smets, F. and R.Wouters (2007), “Shocks and frictions in u.s. business cycles: A Bayesian DSGE approach,”American Economic Review, vol. 97, pages 586—606.

- [30] Solon, G., R. Barsky and J. A. Parker (1994), “Measuring the Cyclicality of Real Wages: How Important is Composition Bias?,”The Quarterly Journal of Economics, vol. 109(1), pages 1—25

24
From the parent’s questionnaire we collected information about the father’s employment status for 2007, 2009 and 2010.

The student questionnaire also asks students about their studying habits such as whether they miss classes, hand in homework late, prepare for exams in advance, and complete homework at school. From the responses to these questions we created four indicator variables.

We construct three di↵erent samples for this study. The first includes all observations of students in the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades in 2007, 2008, 2009 and 2010. This sample constitutes an unbalanced panel of 473,051 observations of 313,297 students (158,180 boys and 155,117 girls).<sup>8</sup> We call this the “full sample”. The second sample encompasses all students that were in 6<sup>_th_</sup> grade in 2007 or in 2008 and were found two years later from the first observation. Therefore, we have exactly two observations for each student. This balanced panel has 48,009 boys and 48,161 girls. We call this the “paired sample”. The third sample also encompasses all that were in 6<sup>_th_</sup> grade in 2007 or in 2008, but we include only those who were also found in next two consecutive years after the first observation. Depending on the exercise, we use the first and third observations or the first and second observation for each students. Therefore, we have exactly two observations for each student. The sample contains observations on 6,563 boys and 6,630 girls and we call this the “3 period sample.”<sup>9</sup> Tables 1 to 3 presents some descriptive statistics of the three samples separately for boys and girls. The incidence of child labor in the full sample is around 12% for boys and 6% for girls. The average age is 14 years old and, on average, boys outperform girls in math and the reverse occurs in Portuguese.

> 8We also dropped 994 students aged nine years old or below. We believe those are measurement error, since the regular age for the 6<sup>_th_</sup> grade in Brazil is 12 years old. Nevertheless, none of the deleted students appear more than once in the sample, therefore the trimming does not change the estimate of the parameter of interest in the fixed e↵ect specification.

> 9No student was age trimmed in the paired and 3 period samples

9