rafmacalaba/datause-encoder-data huggingface dataset:
In summary, we contribute to the literature in several ways. First, we present the largest and most current globally comparable dataset. Second, we include the largest number of developing countries. Third, our dataset contains credible measures of globally comparable achievement distributions as well as mean scores. Fourth, we use multiple methods to link assessments, including mean linking and distribution-related linking methods, enhancing robustness of the dataset. Fifth, we include standard errors for our estimates, enabling explicit quantification of the degree of reliability of each estimate. Finally, this dataset has multiple types of disaggregation enabling targeted as well as equity analysis. Overall, we obtain at least one measure of education quality for approximately 163 countries/areas.

# **2. Data**

# **2.1. International and Regional Standardized Achievement Tests**

This section describes the achievement tests we use to construct our database of _Harmonized Learning Outcomes_ (HLOs) which can be compared globally and over time. We divide the assessments into two main groups: The first consists of international assessments; the second is regional assessments. A detailed summary of these assessments is provided in Table 1.

# **_2.1a. International Standardized Achievement Tests (ISATs)_**

- <u>The Early ISATs (1960 to mid 1990s):</u> **FIMS, FISS, SIMS, SISS, SRC, RLS, MLA and IAEP.** The _International Association for the Evaluation of Educational Achievement_ (IEA) was the first body to measure individual learning achievement for international comparison. Tests began in the early 1960s. These tests were precursors to their more current counterparts: _Trends in International Mathematics and Science Study_ (TIMSS) and _Progress in International Reading Literacy Study_ (PIRLS). The precursors to TIMSS included: pilot studies in 1960, the First International Mathematics Study (FIMS) in 1964, the First International Science Study (FISS) in 1970, the Second International Mathematics Study (SIMS) in 1980-1982, the Second International Mathematics Study (SISS) from 1982-1986, and the _International Assessment of Educational Progress_ (IAEP) conducted in 1988 and 1991. Precursors to PIRLS included: Study of Reading Comprehension Study (SRC) in 1970, and the Reading Literacy Study (RLS) in 1990-1991. According to the test developers, the earlier studies served as a model for the later studies (Campbell, Kelly, Mullis, Martin and Sainsbury, 2001; Elley, 1994).

An additional early international assessment - a joint UNESCO and UNICEF project called the _Monitoring Learning Achievement_ (MLA) program - covers more than 72 countries and ranges from early childhood, basic and secondary education to non-formal adult literacy (Chinapah, 6
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|---|---|
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|Local Empowerment and Environmental Management Project|Foluso Okunmadewa|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Privatization Social Support Project|Ibrahim Akcayoglu|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Forests and Adjacent Lands Management Project|Salimata D. Follea|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Community-Based Urban Development Project|Hassan Madu Kida|
|Public Resource Management Credit Project|Dorsati H. Madani|
19 Table **_5_**

# Education and Fertility

### **Rebson eplaninS** Fe,ity **Buaro** (p. 423)

||Cons<br>-|ODP<br>60|Prim-<br>my<br>.|Second-|_|REV|ASS|_n-y_<br>FF<br>DEV|<br>R|
|---|---|---|---|---|---|---|---|---|---|
||6.08<br>(05)|*0.105<br>(0.069|-156<br>(0.41)|-3.01<br>_(059)_|1.0<br>(13)|-0.13<br>(032)|1.45<br>(035M)|0.40<br>(0.26)|0.72|
|Timeare 98 obs<br>_FUTNEF_<br>ODP60<br>primay<br>Seeoy<br>|ervations in each<br>No of chid<br>ODP**_pwca_**<br>pray<br>sch<br>econdaw<br>y s<br>|regression<br>ren per wo<br>**_pI*_**in 196<br>ool erolme<br>chool enro<br>|an savin<br>0 at intern<br>nt rate in<br>bment rate<br>|<br>bqeond t<br>atoal pricn<br>1960<br>in 1960<br>|he age of 4.<br>|Aenge of<br>|1960.85.<br>|||
|_Xly_<br>REV<br>ASS<br>|avengenti<br>Avenge nu<br>Number of<br>|oof govrn<br>mnber of re<br>asussinatio<br>|ment con<br>volutions**a**<br>ns per ml<br>|sumption (ea<br>**nd****_Cmps_**per<br>ion populati<br>|cluding defe<br>year. 1960<br>on pe year<br>|nce nd edu<br>85.<br>. 196085<br>|cation) to O|DP. 19704|5|
|PPIDEV|Deviation o|f price of|nvetmen|t**goods**from s|ample nme|a In 1960||||

Neither the model nor Barro's data distinguish the education of men from the education of women. Psacharopoulos and Woodhall (1985) argue that in practice there are important differences/. The model focuses on education as affecting the cost of raising children through reducing the labor time that they have available for work, but there are likely to be a number of other important effects. First of all the desire and the ability to control fertility are not the same thing. A reasonable level of education may be necessary for women to be able to understand the consequences of fertility control. Secondly, in societies where women are poorly educated, fertility decisions may be made by men who do not regard the costs of bearing and raising children in quite the same light as do women. Thirdly, the return to education of women may be different from that of men because women are likely to drop out of the labor force for at least part of their working lives. The willingness of parents to educate their daughters may depend on whether society functions by means of a dowry or whether the effects of education can be capitalized into a bride price.

## _2.4 Education and Health_

The relationship between education and health has been discussed by Cochrane et al. (1980) and more recently by Kenkel (1991) and Gomes-Nato et al. (1992). Education feeds through to influencing health by its affects on mortality rates, disease and nutrition. The Cochrane et al. study found education to be an important variable in stemming disease and thus reducing mortality rates. The effect was found to be even stronger than per capita income and doctors per 7Although itcannot be said that the results presented by Rosenzweig (1990) and discussed above support this view.
that any transfer scheme that is ex-ante welfare optimal, is _a fortiori_ ex-post optimal for the incumbent. Hence it is self-enforcing.

Also note that, absent the consumption smoothing motive, the incumbent has no reason to transfer an amount that is larger than the minimum required to disarm the opposition; nor does it have any incentive to use otherwise undesirable transfers to disarm itself, which can be done without bearing the distortionary costs of transfers. However,

**Corollary 2.** _When the opposition has a first mover advantage, there exist ex-post optimal transfers that switch the equilibrium from conflict to insurgency, and these transfers are larger than the optimal ex-ante transfers._

When the opposition has a first mover advantage, and the equilibrium is insurgency, the incumbent knows that the odds of remaining in power and capturing rents in period 2 are low. Hence, it may find it profitable to transfer a large portion of these rents to the first period, when it is sure of consuming part of them.

# **6 Conclusions**

The idea of addressing the oil wealth-conflict nexus by devolution is undoubtedly appealing. To see whether such a tempting idea may work, or it is just too good to be true, in this paper, we introduced oil wealth transfers into a standard model of political conflict, and we analyzed how such transfers affect the incentives of the different parties to fight or to abide by the democratic rules.

As a matter of fact, we did find situations in which redistributing oil revenues prevents conflict and makes all parties better off. However, we also found that transfers may end up fostering violence by decreasing the opportunity cost of mobilization. Other things being equal, small transfers in countries with large oil wealth are of that kind.

We also showed that the way oil wealth is transferred matters. Transferring the money directly to people, an idea that is gaining appeal in policy circles, is more effective in preventing conflicts than fiscal transfers to subnational governments. However, the latter typically generates greater welfare through higher levels of consumption, especially when taxation is relatively inefficient. Similarly, wasting the money, for instance by investing it in pharaonic projects (white elephants) may be the most effective way to prevent conflict but it is also the most costly one.

Looking forward, a promising next step could be that of using the lenses of our model to conduct selected country case studies. In this respect, it would be interesting to use our framework to better understand how countries with different characteristics have responded to the “common shock” of the Arab Spring and how such responses have affected the dynamics of political conflict.

18
The _direct_ effect of regulation on productivity convergence is found to be positive regardless of the proxy used to measure the quality of the regulatory environment. Regarding the _indirect_ effect of regulation, the coefficient of the (Reg∗prodgap)ijt−1 variable is shown to be negative and statistically significant for three out of the five proxies of regulatory environment, suggesting that, at least for these cases, the effect of regulation on the speed of the catch-up process is positive.

The total effect of regulation on productivity growth – when assuming both direct and indirect effects – is also shown to be positive: estimations of the marginal effect of the quality of the regulatory environment on productivity growth points to a positive (and statistically significant) effect for all proxies used (see Table VI in the Annex for the results). Figure 5 below displays the estimated effect on productivity growth due to a one standard variation in each regulatory proxy over the 2003-08 period. Results suggest that productivity dividends are higher when the regulatory framework is measured by the indicator Institutional Environment_DB, which captures the quality of the legal and institutional framework for _protecting investors, getting credit_ and _enforcing contracts._

**Figure 5: Increase in average annual productivity growth (over 2003-08 period) given a one standard deviation**

**increase in each regulatory proxy**

<!-- Start of picture text --> 8.0<br>6.9<br>7.0<br>6.1<br>6.0<br>4.8 4.9<br>5.0<br>3.8<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>percent<br><!-- End of picture text --> _Note: Reg is the regulatory quality indicator, according to WGI data. One standard deviation of each regulatory proxy is computed over the whole sample of countries for the 2003-08 period. See Table VI, Annex, for the respective values considered._ To identify the sectors in which the impact of regulation on productivity growth is higher, the average marginal effect of the regulatory variable was computed.<sup>25</sup> Results show that the marginal impact of the regulatory environment on productivity growth is positive and statistically significant for all sectors, regardless of the proxy used. Table 5 displays the effects on productivity growth associated with a one standard deviation (0.38) increasing of regulatory quality indicator (Reg) over the 2003-08 period.<sup>26</sup> Sectoral

> 25 See Table VIII in the Annex for the estimated marginal effects of all regulatory proxies used.

> 26 The effects on productivity growth due to variation of other proxies of regulatory environment were also computed. Results are available upon request.

13
**Table 1. Summary of data sources**

|**Dimension**|**Dataset Name**|**Unit**|**Resolution**|**Source(s)**|
|---|---|---|---|---|
|Coastline|SRTM v2 Surface<br>Water Body Data|||NASA|
|Elevation|Hydrosheds<br>conditioned SRTM<br>90m DEM|Km<sup>2</sup>|90m|http://gisdata.usgs.net<br>/Website/HydroSHE<br>DS/viewer.php<br>.|
|Watersheds|Hydrosheds<br>Drainage Basins|Km<sup>2</sup>||http://gisdata.usgs.net<br>/Website/HydroSHE<br>DS/viewer.php<br>.|
|Coastline Attributes|DIVA GIS<br>database|||http://diva.demis.nl/fi<br>les/|
|Population|GRUMP 2005 (pre-<br>release) gridded<br>population dataset|Population counts|1km|CIESIN|
|GDP|2005 GDP Surface|Million USD|1km|World Bank , 2008|
|Agricultural Land|Globcover 2.1|Km<sup>2</sup>|300m|http://www.esa.int/<br>due/ionia/globcove<br>r|
|Urban areas|Grump, revised|Km<sup>2</sup>|1km|CIESIN|
|Wetlands|GLWD-3|Km<sup>2</sup>|1km|http://www.worldw<br>ildlife.org/science/d<br>ata/item1877.html<br>|
|Cities|City Polygons with<br>Population Time<br>Series|||Urban Risk Index*|

*Urban extents from GRUMP (alpha) (http://sedac.ciesin.org/gpw/ ) joined with World Cities Data (J. Vernon Henderson 2002). <u>http://www.econ.brown.edu/faculty/henderson/worldcities.html</u>

# **III. Results**

This section first presents global results across regions. Then it examines country results for each of the following five regions: Sub-Saharan Africa, East Asia, Latin America & Caribbean, Middle East & North Africa, and South Asia, and presents a summary of results by most impacted countries for each indicator used in this paper.

12
<!-- Start of picture text --> Figure 1. Growth and Poverty Reduction<br>A. Poverty Headcount B. Real GDP Growth<br>National Moderate Poverty Lines (annual percentage change)<br>60.0 12.0<br>49.1 49.1<br>10.0<br>50.0<br>8.0<br>40.0<br>33.0<br>31.5 6.0<br>30.0<br>23.9 4.0<br>20.0 2.0<br>9.8<br>0.0<br>10.0<br>-2.0<br>0.0<br>-4.0<br>Bangladesh, Peru, 2004- Thailand, 2000-<br>2000-2010 2010 2009 20002001200220032004200520062007200820092010<br>Bangladesh Peru Thailand<br>Initial period Final period<br>Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's Source: WDI, 2011.<br>SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2. Demographic Changes<br>Population Growth Population ages 15-64<br>(annual percentage change) (percent of total population)<br>2.0 75<br>1.8<br>1.6 70<br>1.4<br>1.2 65<br>1.0<br>0.8 60<br>0.6<br>0.4 55<br>0.2<br>0.0 50<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Bangladesh Peru Thailand Bangladesh Peru Thailand<br>Source: WDI, 2011.<br><!-- End of picture text --> 22
public’s water need and generating as much revenue as possible to recover the investment and cost.

Since they only provide the mathematical average value of WTP to society, traditional CVM methods are not capable of accounting for the affordability of low-income groups. For example, the estimated WTP drawn from a discrete choice model is higher than the value acceptable to about half of the respondents, who are usually more sensitive to price change. In other words, these methods are not able to take into account the affordability of vulnerable groups, such as low-income households or populations with poor access to the water supply. Consequently, they cannot completely capture the equity component. Moreover, traditional CVM methods are incapable of providing sufficient information about WTP at the individual level beyond the respondent’s attitude toward the specific proposed price, and thus cannot inform decision makers about how their response would react to price changes.

# **Assessment of Acceptability and Willingness to Pay**

To overcome the shortcomings of the traditional CVM method, this study further develops the multiple-bounded discrete choice model (MBDC), (Welsh and Poe 1998; Wang and Whittington 2005). In this model, affordability, acceptability, and willingness to pay at the individual level can be assessed by a household survey. By applying this improved method, we can estimate the (a) public’s acceptability of each water price; (b) characteristics of respondents in each category of acceptance; (c) WTP; and (d) affordability for each subgroup in the society. By providing more details, this method can help decision makers set the household water price.

Inspired by the “return potential” format used by sociologists to explore the strength of social norms and satisfaction levels across varying conditions, Welsh and Bishop (1993) adapted the MBDC questionnaire for contingent valuation estimation. The MBDC format allows each respondent to vote repeatedly on an ordered sequence of referendum thresholds, thereby reducing the impact of the “anchoring” heuristic, which is often much more important in single or double-bounded questions and provides generally more data and the possibility to verify the coherence and credibility of the data. In the MBDC format, for each price the respondent is asked, a scale of “polychotomous choices”— response options ranging from definitely yes, probably yes, not sure, probably no, to definitely no—is also provided to allow respondents to express their level of voting certainty for the referendum.

The responses to these choices under different bid prices can offer an assessment of the acceptability of the prices. Distributions can be drawn on acceptability with different prices. As shown in Wang and He (2008), the MBDC data can also be used to estimate WTP distributions for each individual. Numerical likelihood values can be assigned to the choices, such as 100 percent for definitely yes, 75 percent for probably yes, 50 percent for not sure, 25 percent for probably no, and 0 percent for definitely no. Simulations can be conducted to assess the sensitivity of the numerical value
adjustment through FSALs as opposed to including financial sector components in nonFSALs (See Appendix 5 for summary statistics for the entire period 1985-95). FSALs did not, however, outperform non-FSALs over the entire period -- post- 1990 FSALs were no more successful than non-FSALs (Table 2.1). In terms of post-intervention improvements in M2, liquid liabilities, and deposit bank credit to the private sector as percentages of GDP (M2/GDP, LL/GDP, and DBPC/GDP, respectively), pre-1990 FSALs substantially outperformed both later ones and non-FSALs from either period.'<sup>2</sup> - Although pre- 1 990 non-FSALs appear to have been the least effective subset "improvements" on the three measures hovered between zero and one percent in absolute value, often with the incorrect sign -- post-1990 interventions (both FSALs and nonFSALs) were not substantially better. The relatively large average improvement in M2/GDP in the wake of post- 1990 FSALs (+3.1%) is driven by one observation, Tanzania (+8.9%). After eliminating it from the sample, the average improvement in M2/GDP was only 1.17%, a figure quite similar to those for non-FSALs. Similarly, the relatively large improvement in M2/GDP and LL/GDP that was associated with post-1990 non-FSALs was driven heavily by Nicaragua. Ignoring that observation, the average improvements in 12 Admittedly, small sample problems make the averages in Table 2.1 somewhat less reliable than one might like. The source of the DBPC, M2, LL, and GDP data is the IMF's International Financial Statistics Yearbook (1996). DBPC includes all assets of deposit money banks classified as claims on the private sector. M2 includes the sum of all currency held outside of banks, demand deposits other than those of the central government, and time, savings, and foreign currency deposits of "resident sectors other than the central government." Liquid liabilities include M2 plus demand and interest bearing liabilities of non-bank financial intermediaries such as savings banks, postal savings institutions, and finance companies. In the individual country data, DBPC data are reported on line 22d, M2 data on line 351, and LL data on 551.

12
22 with po = 0.32857 , p, = -0.181161 and P2 - -0.0010624 Note that we used for the parameters Pa and p2 the values estimated by Boehmer and Megginson (1990) and for the intercept po the value which assures an initial price of debt equal to 0.20 dollar which is approximately the price of Argentina's debt on the secondary market at the beginning of 1991.

For simplification, we suppose that the debtor country's main objective is to maximize its GDP level. As an illustration, let us assume that the utility function can be represented by the following function: (41) u(y) - u(DR, eOD5) - DR*eOD.a with a - 0.041 and I = 0.023 where a and I are the elasticities of Y with respect to DR and eOD5 respectively. Since the response of GDP to debt-reduction and additional financing have been simulated in section 2, we used the estimated parameters presented in table 5. Note that the value of the parameter a reflects the impact of a debt-reduction on GDP without liquidity effect (first column of Table 5).

In order to determine the optimal combination of debt-reduction and liquidity, we maximize the utility function (41) subject to the constraints (38), (39) and (40). Graphically, this maximization problem is illustrated in figure 1. Substituting equations (40) and (39) into (38), we get the curve AA' which represents the combinations of debt-reduction and liquidity for a given amount of loans f and a given buyback price q. The utility function (41) is illustrated by the curve BB'. Since the curve MA' represents the set of deals that leaves remaining bank creditors indifferent, the debtor country will choose from these possible combinations the debt-reduction and liquidity combination which is tangent to the frontier MA'.

figure 1: <!-- Start of picture text --> DR<br>A' O(eODGg)<br><!-- End of picture text --> We present in Table 6 the optimal combinations of debt-reduction and