rafmacalaba/datause-encoder-data huggingface dataset:
The poor in EICV3 can be identified using: and the poor in EICV4 can be identified using: Inequalities (12) and (12’) give a poverty rate of 62.4 percent in EICV3, and 56.9 percent in EICV4. Therefore, if spatial price variations and region level monthly inflation rates are properly controlled, the international poverty rates also show a significant reduction between EICV3 and EICV4.<sup>12</sup>

# **V. Comparison of Price Indices**

The above analysis clearly shows the centrality of adequate price adjustments in poverty measurement, especially when establishing the comparability of poverty statistics over time and across space/regions. In Rwanda, two different sets of price data are potential candidates for poverty measurement – the price data used in the estimation of the official CPI, and the price index developed through household survey data, for instance the COLI in NISR (2016).

Figure 2 shows monthly inflation rates from EICV3 survey months to January 2014 for each of these price indices. The dotted line is a population weighted average of inflation rates calculated from COLI of NISR (2016) for each month from November 2010 (1011) to October 2011 (1110) while the solid line is calculated from monthly CPI (national) data. It is evident that CPI data show much higher inflation rates for almost all survey months. Below we provide some possible explanations underlying these differences.

The first major difference in CPI and COLI comes from the data sets and the reference group used to get item budget shares. A typical price index is made up of item budget weights and prices. In Rwanda, price data for the selected food items included in the NISR (2016) COLI are the same as CPI price data. The budget weights used for the two indices however, differ in two ways. First, in the year 2014, the official CPI base year was set at 2011 and CPI item weights were derived from EICV3 data. In contrast, item weights for the COLIs came from EICV4 data. Second, CPI item weights were computed using all survey households, while COLI used only the bottom 40 percent of households. Thus, both the year and the reference group used to obtain item weights for the two price indices are different.

The second major difference in CPI and COLI relates to the level of aggregation of the two indices. The CPI is estimated for national, rural and urban regions, while the COLI is estimated for 5 provinces.

> 12 Inequalities (12) and (12’) use a unique poverty line expressed in January 2014 prices. But as shown above, the global poverty line uses a poverty line expressed in prices of an average month of a household survey, i.e., IPL��� ��� �����  for EICV3 survey (2010/11) and IPL��� ��� ����� for EICV4 (2013/14). Appendix 2 shows how inequalities (12) and (12’) can be modified so that (i) poverty rates do not change but (ii) IPL��� ��� �����  and IPL��� ��� �����  are used as the poverty line for EICV3 and EICV4, respectively.

18
Long-term debt (as a share of total debt) has been low across the whole period in all East Asian countries (Table 7). Malaysia, Taiwan and Thailand stand out with less than 1/3. Japan and the Philippines have the highest share, **l/2,** while the others are about 0.43. In contrast, about **3/4** of debt of US corporates is long term, while in Germany the ratio is 0.55. In spite of the large attention to the role of short-term debt in the East Asian financial crisis, these data do not suggest a massive buildup in short-term debt for the East Asian countries, at least up to the end of 1996, but rather a consistently low share of long-term debt. In fact, only Japan saw some decrease in the share of long-term debt. As these data do not distinguish foreign exchange from domestic debt, it can of course be that the composition may have shifted away from short-term domestic debt toward short-term foreign exchange debt.

**Table** 7: **Long Term Debt Share for Nine Asian Countries, Germany, and the US**

|||||(%,<br>|median<br>|s)|||||
|---|---|---|---|---|---|---|---|---|---|---|
|County<br>||1988|1989||1990|1991|J1992|1993|1994|1995|1996|[1988-96|
|Hong Kong|59.7|59.5|53.8|_56.5_|44.7|44.7|40.7|37.3|36.4|44.9|
|Indonesia|n.a.|n.a.|n.a.|52.4|40.8|39.6|41.6|41.8|43.3|43.1|
|Japan|49.9|54.1|53.8|49.9|49.4|51.7|47.7|44.4|40.8|48.4|
|Korea|55.7|47.2|49.8|49.8|44.2|43.7|41.4|40.4|41.5|43.7|
|Malaysia|35.8|35.5|32.5|27.1|26.9|26.6|27.2|27.8|29.9|29.2|
|Philippines|n.a.|na.|n.a.|57.2|53.1|50.3|50.2|49.8|51.4|52.2|
|Singapore|57.2|55.4|54.1|33.8|33.8|33.9|40.2|38.6|41.1|43.3|
|Taiwan|n.a.|n.a.|n.a.|53.9|44.4|32.8|34.6|34.3|38.9|35.9|
|Thailand|58.1|49.8|38.8|34.3|25.2|26.4|27.6|32.9|32.8|30.9|
|US|77.7|77.2|76.3|76.7|75.8|76.2|75.2|74.6|74.1|75.9|
|Germany|56.8|55.4|54.5|53.9|55.2|55.4|55.4|55.3|54.7|55.3|

Note: Table A6 reports means, standard deviations, and sample sizes.

The international comparison of the maturity of debt structure (Figure 3) reveals that most East Asian countries rank below European and Latin American countries in their share of long term debt.<sup>4</sup> Among East Asian countries, only corporations from the Philippines have an average share of long-term debt greater than 50%. There is a general tendency for corporates in richer countries to have more long-term debt, as observed by Demirguc-Kunt and Maksimovic (1998) and others. Some other, low-incomeAsian countries (Sri Lanka, Pakistan, China) have indeed low shares of long term debt. But many of the higher-income East Asian countries are outliers to this pattern, as they rely less on long-term debt than what would be expected on the basis of their per-capita income level. Japan, for example, ranks below many other OECDcountries. Among developing countries, Chile stands out as country with a very high share of long-term debt.

4We present the share of long-term debt, rather than the share of short-term debt as the latter can underestimate the amount of liabilities with a short maturity as it excludes, for example, trade credits.

11
Table 5: Saving Rate by Income, Wealth, and Land Partition (Malawi ISA 2010)

||B|ottom (%|)|||Quintile|s|||Top (%)||All|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0-1|1-5|5-10|1st|2nd|3rd|4th|5th|10-5|5-1|1|0-100|
||||||(A) Ru|ral Resi|dency||||||
|Saving rate :|||||||||||||
|By income partition|n.a.|-7.20|-3.82|-3.55|-1.41|-0.87|-0.37|0.30|0.11|0.37|.0.72|-0.23|
|By wealth partition|-0.79|-0.66|-0.50|-0.60|-0.25|-0.26|-0.28|-0.06|-0.11|0.09|.0.08|-0.23|
|By land partition|-0.22|n.a.|n.a.|-0.26|-0.33|-0.32|-0.25|-0.09|0.00|-0.10|0.13|-0.23|

||||||(B) Ur|ban Res|idency||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Saving rate:|||||||||||||
|By income partition|n.a.|-29.69|-9.11|-7.76|-2.07|-1.23|-0.60|0.35|-0.02|0.36|0.74|-0.15|
|By wealth partition|-0.77|-0.56|-0.54|-0.87|-1.03|-0.38|-0.33|0.18|0.18|0.34|0.42|-0.15|
|By land partition|-0.21|n.a.|n.a.|n.a.|n.a.|-0.21|-0.31|0.06|0.24|0.08|0.15|-0.15|

_Notes:_ Household saving rates are defined as 1 minus the household consumption to income ratio.
Bank 2015). Valin et al. (2014) find a median income elasticity of demand of close to 0.1 for rice and wheat. Elasticities are generally higher for foods with higher fat and protein contents, such as animal products, suggesting that consumers switch to these types of foods as incomes rise (Salois, Tiffin, and Balcombe 2012; Valin et al. 2014, World Bank 2015). The use of maize and soybeans as animal feed means that their elasticities are driven more by demand for meat than demand for direct consumption, resulting in higher elasticities. For example, 70 percent of soybeans in the United States are used for animal feed (USDA 2015).

## **2.2 Price elasticities of demand**

Demand for commodities tends to be price inelastic. Within energy, price elasticities for crude oil range from zero to -0.4 (Huntington, Barrios, and Arora 2017; Dahl and Roman 2004). For metals, Stuermer (2017) finds the largest price elasticity for aluminum (-0.7), but smaller elasticities for copper (-0.4), tin, and zinc (less than or equal to -0.2), while Fernandez (2018) generally finds modest price elasticities for metals. As with income elasticities, price elasticities of demand tend to be larger in the long-run than the short-run, as consumers have more time to respond to changes in prices by finding substitutes, or efficiency gains. For example, Dahl and Roman (2004) find a short-run price elasticity of crude oil of -0.11, and a long-run price elasticity of -0.43.

# **3. Estimation of long-run income elasticities**

This section estimates long-run income elasticities of the energy, metals, and agricultural commodities listed earlier, using an auto-regressive distributed lag model. The methodology is similar to that of Fernandez (2018).

## **3.1. Model specification**

The empirical approach adopted in this paper is based on the pooled mean group (PMG) autoregressive distributed lag (ARDL) ( _p,q,r_ ) model developed by Pesaran et al. (1999), where _p_ , _q_ , and _r_ are respectively the lag length of the dependent variable and the two explanatory variables. The model is of the following form: where cij,t is the logarithm of real per capita consumption (in millions of tonnes for metals and agricultural commodities and in tonnes of oil equivalent for energy commodities) of commodity _i_ for country _j_ at year _t_ ; yij,t is real per capita income for country _j_ at year _t_ ; pij,t denotes the world price of commodity _i_ , αij represents country fixed effects, and εij,t is the stochastic error 2 term which has zero mean and constant variance. The quadratic term, yij,t, in equation (1) accounts for nonlinearity inherent in most demand functions which, in this case, represents the level at which income plateaus.

The error correction form of equation (1) is: 7
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48
of recent immigrants in the grade by one standard deviation (5 percentage points) would reduce natives’ average test scores by 0.03 standard deviation. Increasing the concentration of recent immigrants in the classroom by one standard deviation, holding class size constant, has therefore a quantitatively similar effect as adding one student to the classroom.

In terms of spending, the STAR project raised costs by about 30% in K-3, and was estimated to raise test scores by 0.17 standard deviation (Krueger and Whitmore (2001)). Spending per pupil in at the time was around $9,000, so comparable proportional class size reductions would cost around $2,700 per pupil per year, resulting in a positive effect size of around 0.06 for $1,000, assuming linear effects of school spending on test scores. Increasing the share of recent migrants in the classroom by one standard deviation (5 percentage points) therefore has a comparable effect as decreasing expenditure per student by $500 a year.

It is also useful to compare the magnitude of the effects estimated to other studies on peer effects in the classroom. Our estimates of the effect of recent migrants are considerably smaller than that of peer socio-economic background (proxied by books at home) reported by Ammermueller and Pischke (2009) in the European context. Their estimates are drawn from a sample and context similar to ours, using a sample of third graders in European primary schools. They report large effects compared to the US literature and find that a one standard deviation change in their peer composition variable - the average number of books in the home of classroom peers - increases reading test scores by 0.17 of a standard deviation. Other studies on school peer effects typically report size effects ranging from 0.03 to 0.10 of a standard deviation (Lavy et al. (2012a), Duflo et al. (2011)). Our estimated effect of the share of recent migrant in the grade is therefore at the lower end of that range.<sup>22</sup>

> 22The effect size is similar to that of Lavy et al. (2012a), who find that increasing the share of

30
# **Figure 7:  Business registration modernization**

<!-- Start of picture text --> 100%<br>90% High Income Upper Middle Income Lower Middle Income Lower Income<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Electronic data storage Data Remote Access Registration outside Remote registration Internet Registration<br>Capital City<br><!-- End of picture text --> Source: 2008 World Bank Group Entrepreneurship Survey.

Multiple elements can achieve this goal. For example, registries in developing countries might start by offering entrepreneurs the ability to retreive information on a web site (such as laws and regulations), download registration forms (but not necessarily to submit them on-line), and check available firm names. Governments may need to provide a centralized interface for a regional system, such as by merging local courts’ business registries into a central registry database. Countries that already have a centralized registry but are still paper-based need to digitize historical and automate new data entries by using networked computers and online forms. Registries that are already automated need to implement secure, legal authentication methods, such as digital signatures, to remove the last vestiges of in-person or in-paper requirements. Registries that aim to benefit from further time and cost-savings would interlink the electronic business with other e-Government services, such as e-Tax, e-Customs or e-Procurement applications, for additonal cost- and time-efficiencies for governments.

It is difficult to isolate the causality between the implementation of electronic registration, improvements in the business environment, and the creation of new businesses. Nevertheless, the data collected by the WBGES 2008 suggest that business registry modernization provides a more favorable business environment for starting a business and facilitates the business creation process. On average, countries with remote 17
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 3: Regional Tariff Changes <!-- Start of picture text --> BelémBelém<br>ManausManaus<br>FortalezaFortaleza<br>RecifeRecife<br>SalvadorSalvador<br>BrasíliaBrasília<br>-17% to -12%<br>-12% to -10%<br>Belo HorizonteBelo Horizonte<br>-10% to -9%<br>-9% to -6%<br>São PauloSão Paulo<br>-6% to +1.4% CuritibaCuritiba<br>Porto AlegrePorto Alegre<br><!-- End of picture text --> Local labor markets reflect microregions defined by IBGE, aggregated slightly to account for border changes between 1986 and 2010. Regions are colored based on the regional tariff change measure defined in (3). Dark lines represent state borders, gray lines represent consistent microregion borders, and cross-hatched migroregions are omitted from the analysis. These microregions were either i) part of a Free Trade Area ii) part of the state of Tocantins and not consistently identifiable over time, or iii) not included in the sample before 1990.

42
commitments seldom go beyond countries’ applied policies, the explicit liberalization resulting from the agreement was limited to a few members and a few areas.<sup>2</sup> Figure 2: Services Trade Restrictiveness Indices for TPP Countries Doha Offers, TPP Commitments and Applied Policy <!-- Start of picture text --> Overall STRI<br>BRN MYS CHL SGP MEX VNM CAN USA PER JPN AUS NZL<br>Doha TPP Policy<br>Note: For Brunei, applied policy information is missing.<br>Absense of a bar means the score is zero<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Note 1: BRN =Brunei, MYS =Malaysia, CHL = Chile, SGP= Singapore, MEX= Mexico, CAN= Canada, VNM = Vietnam, USA =United States, PER = Peru, JPN= Japan, AUS = Australia; NLZ = New Zealand. Note 2: The country STRI is an aggregate score of indices for services sectors covered, the country STRI comprises STRIs of five major sectors, financial, telecom, retailing, transportation, and professional services. These five sectors contain different subsectors and the relevant modes.

Source: Gootiiz and Mattoo (2017) , drawing on the World Bank TPP database, http://iresearch.worldbank.org/servicetrade/TPPservices.htm.

> 2 However, in comparing TPP commitments with the policy status quo, our measures may underestimate the liberalizing impact of the TPP in two ways.  First, the TPP negotiating process could already have induced changes in applied policies over the course of the negotiations; for example, Mexico’s telecommunications policy was reformed in 2013, and Malaysia carried out several reforms between 2009 and 2013 in financial services, telecommunications, and professional services.  Second, some of the TPP commitments specify liberalization several years after the completion of the agreement, for example by Vietnam in telecommunications and retail services and by Malaysia in the financial sector.

6
32 Table 4: Effect of Attending a Single-Sex School on Stereotypic View of Mastematics in Nigeria, 1981-82

|Variable|Beta<br>Coefficienta,b/|Regression<br>Coefficient-/|Standard<br>Error|
|---|---|---|---|
|School sex type:<br>Girls'school|-. 10*|-. 46|.21|
|Boys' school|.08*|.21|.09|
|Badckgoud:||||
|Sex, fewale|-. 35***|-1.08|.12|
|Prof.father|-.06*|-. 20|.10|
|Test score|-. 03|-. 01|.10|
|Attituxdes:||||
|Mbtivation|.16***|.26|.05|
|Teachers, teaching:||||
|Teacher sex (female)|.02|.06|.11|
|Time,evaluation|-. 07|-. G0|.00|
|Time,eval.,squareW|.04|.00|.00|
|Constant||3.35||
|R2||.24||

Note:

- (a) These are standardized regression coefficients, equivalent to adjusted correlations between the indeperdent variable after having taken the other fidependent variables in the model into account. We interpret these as effect sizes.

- (b) Nadnial significanrce levels of regression coefficients are as follows: *-p<.05; *s-p<.Ol: ***=p<.001. Determined as t-statistics compputed by dividing the unstandardized regression coefficient by its standard error.

- (c) These are unstandardized regression coefficients.

- (d) The relationship between stereotypic view of ma-n and the teacher's tine spent on monitoring and evaluation had both linear and quadratic ccmponrnts. This variable represents the quadratic ccuponent.
||Iwa|**PRE Working Paper**Series<br>_ALAho_|_DA19_|Contact<br>for gpape|
|---|---|---|---|---|
|WPS388|The Distortionary Effects of Tariff<br>Exemptions in Argentina|Faezeh Foroutan|March 1990|S. Fallon<br>38009|
|WPS389|Monetary Cooperation in the CFA<br>Zone|Patrick Honohan|March 1990|Wilal Pitayato-<br>nakam<br>37666|
|WPS390|Price and Monetary Convergence<br>in Currency Unions: The Franc and<br>Rand Zones|Patrick Honohan|March 1990|Wilai Pitayato-<br>nakam<br>37666|
|WPS391|Weafth Effects of Voluntary Debt<br>Reduction in Latin America|Daniel Oks|April 1990|S. King-Watson<br>31047|
|WPS392|Institutional Development in World<br>Bank Projects: A Cross-Sectional<br>Review|Samuel Paul|April 1990|E. Madrona<br>37489|
|WPS393|Debt-for-Nature Swaps|Michael Occhiolini|March 1990|S. King-Watson<br>31047|
|WPS394|Threshold Effects in International<br>Lending|Mark M. Spiegel|April 1990|S. King-Watson<br>31047|
|WPS395|How Gambians Save-<br>and What<br>Their Strategies Imply for Internatio<br>Aid|Parker Shipton<br>nal|April 1990|C. Spooner<br>30464|
|WPS396|Strategic Trade Policy: How New?<br>How Sensible?|Max Corden|||
|WPS397|Antidumping Regulations or<br>Procartel Law? The EC Chemical<br>Cases|Patrick A. Messerlin|April 1990|S. Torrijos<br>33709|
|WPS398|Agricultural Extension for Women<br>Farmers in Africa|Katrine A. Saito<br>C. Jean Weidemann|April 1990|M. Villar<br>33752|
|WPS399|Macroeconomic Adjustment,<br>Stabilization, and Growth in<br>Reforming Socialist Economies:<br>Analytical and Policy Issues|Andr6s Solimano|April 1990|E. Khine<br>39361|
|WPS400|Macroeconomic Constraints for<br>Medium-Term Growth and Distribujt<br>A Model for Chile|Andr6s Solimano<br>ion:|April 1990|E. Khine<br>39361|
|WPS401|Policing Unfair Imports: The U.S.<br>Example|J. Michael Finger<br>Tracy Murray|March 1990|N. Artis<br>38010|
UNODC ́s methodology for collecting data on coca cultivation covers almost the entire territories in the producer countries, whereas ONDCP ́s only covers a representative sample. UNODC also makes more corrections than ONDCP for possible biases and mistakes in the interpretation of aerial imagery. Finally, UNODC has been actively involved in conducting the “Coca Cultivation Surveys” in each one of the producer countries, which are complemented by continous efforts to undertake field studies to update parameters such as yields per hectare, an important parameter for coming up with and estimating potential cocaine production. Although UNODC stands as a more reliable source of data on coca cultivation, cocaine production, and related issues, we will be referring to the two data sources in the following section of the paper in order to compare them to each other (if possible).

# **COCAINE PRODUCTION: STYLIZED FACTS**

# i. Coca Cultivation

According to ONDCP, coca cultivation in the three Andean countries remained relatively stable throughout the 1990s. On average, coca was cultivated in about 200,000 hectares in the three producer countries, but the share of each country in total coca cultivation changed dramatically during the decade. While Peru had the largest number of hectares in 1990 (about 57% of the total) and Colombia the lowest (19%), by 1999 these shares had completely reversed, with Peru having 21% of the total, Bolivia 12%, and Colombia 67% (Figure 1). On one hand, this change was, in part, a result of increasing eradication efforts undertaken by the Bolivian and Peruvian governments, and of aerial interdiction efforts undertaken by the Peruvian government to close the air bridge between coca producing 8