rafmacalaba/datause-encoder-data huggingface dataset:
with the exception of A _Indt-l -_ their significance in the short-term. Industrial growth only seems to have a direct positive impact on service sector growth.

Table 8: Dynamic Short-Run Sector Growth Model (FIML estimation)

|A Agr,|A Indt|A Sert|A Agrtl.|AIndt-l|A Ser,-<br>1|CIt.,|
|---|---|---|---|---|---|---|
|AAgrt<br>-1<br>AIndt<br>ASert|-1|-1|0.94599<br>(3.805)<br>0.24592<br>(1.679)|0.16285*<br>(2.005)|-0.33381<br>(-1.874)<br>-0.72683<br>(-2.272)<br>-0.53421<br>(2.908)|0.34612<br>(4.471)<br>0.19976<br>(1.497)<br>0.18986<br>(-2.814)|
|**_Diagnostics_**<br>_Single Equation_|3||F,(2,30)|2<br>**_Xn,o,n_**(2)|F,ch(1,30)||
|AAgr|0.01264||0.736|1.841|3.615||
|AInd<br>A Ser<br>_Vector Analysis_|0.02155<br>0.01201<br>Fa (18,71)=<br>_nonn_(6)=<br>F, (84,78|1.4329<br> 6.3 53<br>)=0.8523|_1.585_<br>4.007|1.668<br>2.272|1.139<br>1.037||

* Rejection at a _5_ percent level of significance.

_Note:_ Values in parentheses are t-statistics. A indicates a variable in first differences, subscripts denotes the time period, i.e., t: current period, t-1: lagged one period, CI denotes the restricted cointegration relation.

_Source:_ Authors' calculations The agricultural sector seems to play a major role in determining growth in the other two sectors. The strong positive growth effect of agriculture on the industrial sector is of particular interest since it indicates direct _Granger_ causality from agriculture to industry. An explanation for the direct linkage between the agricultural and industrial sector could be provided by the fact that the marine product and processed food industry, which depends directly on agriculture and fishing, managed to increase their overall export shares substantially over recent years.

While there appears to be a direct impact of agricultural growth on industrial growth, the industrial sector affects the growth of the agricultural sector only indirectly via the error correction term and via the growth equation of the service sector, _ASer,1.._ Growth in the agricultural sector also seems to positively affect growth in the service sector, possibly indicating ani increase in commerce with agricultural produces.

15
foreign language, although they deteriorate less for those in math-oriented and foreign languageoriented programs. We do not find evidence of statistically significant differences in relative learning mobility for native language (Spanish). <mark>In this sense this paper deals with teachers’ academic competencies and also contributes to the</mark> _<mark>value added of higher education</mark>_ <mark>literature (Klein et al. 2005; Liu 2011; Saavedra 2009; Saavedra and Saavedra 2011; Cunha and Miller 2014).</mark> <mark>The rest of the paper proceeds as follows. In the next section we introduce the data sources and present some descriptive statistics. Section three introduces the concept of relative learning mobility. In the fourth section we present the main empirical analysis. In the fifth section we conclude.</mark>

# **2.** **<mark>Data and descriptive statistics</mark>**

<mark>The data come from</mark> _<mark>Instituto Colombiano para la Evaluación de la Educación</mark>_ <mark>(ICFES). We use data from two national standardized tests: Saber 11 (2002-2007) and Saber Pro (2011).</mark> _<mark>Saber 11</mark>_ <mark>assesses senior year high school students’ academic competencies in language (Spanish), mathematics, biology, chemistry, physics, social sciences, philosophy and foreign language (English).</mark> _<mark>Saber Pro</mark>_ <mark>assesses the academic competences of higher education students who have completed at least 75% of their academic programs, on program specific areas (e.g., engineers are assessed in engineering; economists in economics; biologists in biology, and so on</mark> ). The novelty that makes this paper feasible is that starting on the second semester of <mark>2011, Saber Pro also assesses all students, regardless of their major, in quantitative reasoning, reading, writing and English as foreign language.</mark><sup>2</sup> <mark>Saber 11 is mandatory for all senior year high school students who wish to obtain their school degree, and serves as an input for college entrance. In fact, in many universities this is the sole admission criterion (Saavedra and Saavedra, 2011). Saber Pro is mandatory for those students who</mark>

> 2 See: http://www.icfes.gov.co/examenes/.

4
**Figure 5: China's FDI in Natural Resources**

<!-- Start of picture text --> (share of China's total FDI (left scale); share of Global FDI in<br>natural resources (right scale))<br>100 20<br>90 18<br>80 16<br>70 14<br>60 12<br>50 10<br>40 8<br>30 6<br>20 4<br>10 2<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010<br>Ratio to Total FDI in Natural Resource (%)<br>Ratio of Natural Resources to Total FDI (%)<br><!-- End of picture text --> Source: Kierkegaard (forthcoming 2012).

Finally, exclusivity in access can arise by restricting exports of resources where a country itself has a major share of the resource. In China‘s case, concerns have already arisen, as discussed above, because it restricted exports of rare earth metals, for some of which (e.g. scandium and yttrium) it accounts for more than 70 percent of the world‘s exports, as well as of other key raw materials, such as various forms of bauxite, magnesium, and zinc, where again it accounts for a large share of world exports.

# **V. Anticipating Dominance:  Implications for Policy and International Cooperation**

The high probability scenario is one of China having a vested interest in an open trading system and hence acting, even leading, to preserve it. But we cannot be sure – even the United States was tempted to manipulate the trading system to its own advantage, as discussed above.

From the Chinese perspective, there will inevitably be a weighing of the benefits of adhering to multilateral principles and processes – in terms of economic efficiency, low transactions costs 22
<!-- Start of picture text --> ~<br>AS CS ea<br>o% Oye<br>*<br>wi. MAG iss<br>mo5 2 1 al= Uesig uredoie | me coteMoserSOE)snr (nena8<br>x Ee Fiomsnopeis BB crweoss (1073)<br>ay<br><!-- End of picture text -->

<!-- Start of picture text --> he oe<br>ae Bisons men<br>Soph a Bf Florianapelin Bszian Tr)<br><!-- End of picture text --> <!-- Start of picture text --> (AS<br>“aie<br>Wy eePe<br>. 7<br>;<br>or st ne¥ i “4 "0 LJoztermGinase (nonis<br>Pedant Floriamdpolin Biivocs ou)<br>Ly<br><!-- End of picture text -->
<!-- Start of picture text --> 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>(a) Copper price (b) Fed funds rate<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>(c) Money (M2) (d) Output gap<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>(e) Exchange rate (f) Consumer Price Index<br>Notes: The solid lines describe the variables in levels and native units (see left y-axis), the dashed lines indicate are the<br>first differences of the logarithm of these series. The dotted vertical line at the beginning of 2015 indicates the end of the<br>sample period used for our main specification. See main text for further transformations applied to the series.<br>.4 1<br>10000<br>6.00<br>.2<br>8000 0<br>0 4.00<br>6000 −1<br>−.2 Percent<br>log difference log difference<br>USD / metric ton<br>4000 −.4 2.00 −2<br>2000 −.6<br>0.00 −3<br>.2 10 6<br>40<br>.15<br>4<br>30 5<br>.1<br>2<br>20<br>.05<br>Billions of ZMW log difference 0 log difference<br>0<br>10 0<br>Percent deviation from potential output<br>0 −.05 −5 −2<br>1.5 .2 200 .15<br>.1<br>150<br>1 .1<br>0<br>100<br>USD/ZMW −.1 log difference log difference<br>.5 Index (2009=100) .05<br>50<br>−.2<br>0 −.3 0 0<br><!-- End of picture text --> _Figure 2: Variables in levels and log differences_ 11
per country in the 2023 round. For this analysis, we used a subset of 30 countries surveyed in both rounds of the LiTS.<sup>2</sup> We capture support for tax-financed investments in climate change actions by examining responses to the question “ _Would you be willing to give up part of your income or pay more taxes if you were sure that the extra money was spent to combat climate change?_ ” We create a dummy with a value of 1 if the respondent responded yes and 0 if he or she responded no or don’t know. This dummy variable is the main dependent variable of our analysis. Our data also contain information on age, gender, education, marital status, religion, and type of residence, which we use as controls in our empirical analysis.

We supplement the data from the LiTS with the Google Trends index, which is calculated by taking the total number of searches for a specific term at a given location and within a given time frame, dividing it by the total number of all searches conducted at that location and during the period, and then scaling this ratio with respect to the highest value in the series. This normalization process precluded us from comparing search interest indexes over time (i.e., <u>Mavragani and Ochoa 2019). To overcome this problem, we present the comparisons of the changes in the index with the</u> changes in the search index for the topics we consider relevant and comparable with the issue of climate change.

We also use administrative data on several environmental risks at the primary sampling unit (PSU) level,<sup>3</sup> capturing means, standard deviations, ranges of temperature and precipitation, information on air pollution, and the severity of flooding. We use PSU-level statistics on the risk factors for the seven years before each survey. We derive a weighted average of each risk factor with weights linearly inversely related to the time from the survey date (the further away the year is from the survey date, the lower the weight). We also tested specifications using principal component analysis or quadratic weights to aggregate seven years of risk factor information. Qualitatively,

> 2 This subset comprises countries that belonged to the former Soviet Union (except Ukraine and Turkmenistan), countries of the former Yugoslavia, Germany, Greece, Mongolia, Türkiye, and countries in Central and Eastern Europe that transitioned from a planned to a market economy during the 1990s. We excluded from the sample six countries in the Middle East and North Africa that were only included in the last round (Algeria, Jordan, Lebanon, Morocco, Tunisia, and the West Bank and Gaza).

> 3 We have this administrative data for all countries in our sample except 9 countries in the Middle East, North Africa, and Western Europe.

5
# 1: **Introduction**

The objective of this paper is to empirically test across alternative, apparently observationally equivalent theories of currency crises that are often difficult to distinguish from each other based on the behavior of commonly used predictors. Using a new, comprehensive data set on gross extemal assets and liabilities for 167 countries, this study is able to make a significant move towards redressing this shortcoming. The focus is on identifying potential crisis predictors as well as testing the validity of the distinct transmission mechanisms implied by various theories of crisis. There is strong evidence in support of insurance-based models suggesting that proxies for the accumulation of contingent liabilities are an effective crisis predictor.

There is a large and growing literature that emphasizes the role of capital flows and/or imperfect domestic financial intermediation in generating crises. This "third-generation" of currency crisis models (Dooley, 1997; Chang and Velasco, 1998; iCalvo, 1993), focuses on the central bank's function as lender of last resort as a contributing factor to moral hazard in domestic banking (insurance models), mismatched external assets and liabilities (illiquidity models) and volatile capital flows to emerging markets in fixed exchange rate environments ("sudden stop" models and the literature on the optimal sequencing of capital account liberalization). Common prescriptions are imposing capital account restrictions and IMF lending to bolster international reserves.

No one would disagree that a well-regulated, deep financial sector is desirable. But in policy prescriptions, what weight should be assigned to central bank guarantees, or to the accumulation of net short-term debt by the domestic banking sector? Certainly, they are closely related, but the relevant institutional response would be different. In the absence of exchange or capital controls, any convertible exchange rate will ensure that the economy is illiquid always. The central bank must stand ready to exchange domestic currency for its liquid foreign assets even though its currency liabilities will always be the greater of the two quantities. Since a currency crisis is always a potential equilibrium under this logic, the root of the problem is likely to lie elsewhere, say in insurance/moral hazard, or in domestic banking failures or in inconsistent domestic macroeconomic policies. A related question is 2
# **Table 8.A. Model comparison: Bias of nickel price forecasts**

(U.S. dollars per metric tonne)

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|-545.8|...|...|...|...|...|
|Futures|3||-221|...|...|...|...|
|Bivariate correlations|3|||-125.2|...|...|...|
|BVAR|3||||-309.1|...|...|
|OEM|3|||||-222|...|
|Machine learning|3||*|*||*|-1279.4|
|Consensus|6|-343.7|...|...|...|...|...|
|Futures|6||-193.4|...|...|...|...|
|Bivariate correlations|6|||1122.9|...|...|...|
|BVAR|6||||-266.7|...|...|
|OEM|6|||||-126.6|...|
|Machine learning|6||||||-1089.7|
|Consensus|9|-167.4|...|...|...|...|...|
|Futures|9||-161.6|...|...|...|...|
|Bivariate correlations|9||*|1056.8|...|...|...|
|BVAR|9||||-333|...|...|
|OEM|9|||*||-127.5|...|
|Machine learning|9||||||-1143.2|
|Consensus|12|21.6|...|...|...|...|...|
|Futures|12||-131.9|...|...|...|...|
|Bivariate correlations|12|*|*|1950|...|...|...|
|BVAR|12|||*|-154.6|...|...|
|OEM|12|||*||22.6|...|
|Machine learning|12||*||||-1276.2|
|Consensus|15|180.7|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15||...|...|-393||...|
|OEM|15||...|...||252.5|...|
|Machine learning|15||...|...|||-1371.1|
|Consensus|18|295.9|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18||...|...|-371|...|...|
|OEM|18||...|...||441.9|...|
|Machine learning|18||...|...|||-1407.3|
|Consensus|21|425.4|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21|...|...|...|...|...|...|
|BVAR|21||...|...|623.9|...|...|
|OEM|21||...|...||685.3|...|
|Machine learning|21||...|...|||-1386.5|
|Consensus|24|597.6|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|1136.4|...|...|
|OEM|24||...|...||477.3|...|
|Machine learning|24||...|...|||-1314.8|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias).

46
development program in the Philippines, administrative data indicate that the timing of the implementation differed substantially across program areas; because of lags in central procurement, not all providers in the program areas had received required training three years after project launch (Armecin et al. 2006). Besides material inputs, snags in information flows and project finances can also delay implementation. Delays in providing the information about intended household beneficiaries in Mexico’s and Ecuador’s conditional cash transfer programs prevented program operators in some sites from making punctual transfers to households (Rawlings and Rubio 2005; Schady and Araujo 2008).<sup>7</sup> In Argentina, the targeting performance of its Trabajar program was weakened by poor municipalities having a harder time raising cofinancing required for the sub-projects (Ravallion 2000).

The problem of implementation lags can be partly addressed if careful and complete administrative data on timing are available for the program; cross-referencing such data with information from public officials or community leaders in treatment areas could reveal the institutional reasons for variation in implementation. For example, if there is an average gap of one year between program launch and actual implementation, then it is reasonable for the evaluation to make an allowance of one year after program launch before estimating program impact.<sup>8</sup> However, actual information on dates often is not readily available so studies have tended to allot an arbitrary grace period to account for lags.

Assuming a constant allowance for delays, moreover, may not be an adequate solution if there is wide variation in the timing of implementation across treatment areas as a result of the program involving a large number of geographical regions or a large number of components and actors. In programs that cover several states or provinces, region or state fixed effects might control for duration differences if the differences are homogeneous within a region or state and if 8
# Targeting Assistance to the Poor: A Multivariate Approach Using Household Survey Data

by Paul Glewwe and Oussama Kanaan

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Principles of Targeting Transfers to the Poor|2|
|III.|Multivariate Tarteting Using Survey Data|7|
|IV.|Application of the Method to Survey Data from<br>Cote d'Ivoire|14|
||A. Welfare Levels and Urban Household Characteristics|15|
||B. Welfare Levels and Rural Household Characteristics|17|
||C. Targeting Transfers in Urban CMte d'Ivoire<br>Targeting Transfers in Rural C6te d'Ivoire<br>E. Allocating Funds Between Urban and Rural Areas|20<br>26<br>29|
|V.|Extensions and Complications|31|
|Vi.|Conclusion|35|
||Tables|37|
||References|49|

We would like to thank Harold Alderman, Tim Besley, Nanak Kakwani, and Elene Makonnen for comments on an earlier draft of this paper.
# **_Sample Design_**

The wide variation in HIV prevalence both across and within African countries, especially the low prevalence in some countries, requires alternatives to random sample designs (or the traditional two-stage random designs used by most household surveys, including the DHS). About two-thirds of the population in Sub-Saharan Africa resides in countries with prevalence of less than 5 percent (see appendix Table 1). Among the 18 poorest countries in Sub-Saharan Africa (countries with GNI per capita of $350 or less), the prevalence of HIV in 11 of them (comprising more than 70 percent of this population of 324 million) is less than 5 percent. Even in high-prevalence countries, substantial regional variation exists. In the North Eastern province of Kenya, for example, none of the DHS sample of men ( _N_ = 48) or women ( _N_ = 60) tested positive for HIV (CBS, MOH, and ORC Macro 2004). To ensure sufficient sample sizes, researchers often use purposive sample designs (albeit maybe still random). Such designs may make it difficult to generalize results, raising the question of external validity. Clark’s (2004) careful study on early marriage, discussed later in this paper, draws on an urban sample of young women in Kenya and Zambia of 167 and 135 respondents, respectively. This reflects the challenge of conducting detailed analysis with integrated socioeconomic and HIV-status data, but having samples sufficient to describe national situations.

# **_Understanding the Correlates of HIV_**

Quantitative studies of socioeconomic status and HIV entail analysis of survey data to produce correlations or, depending on the data and techniques, identify causal relations. The relationship identified between indicators of socioeconomic status and HIV status (or proximate measures) depends on the models used. The least complex approach is to examine bivariate relations, but bivariate correlations between a single indicator of socioeconomic status and HIV can produce misleading results, in part because of the multiple underlying factors that any one socioeconomic status measure captures.

Consider simple examples from Ethiopia and Kenya (Table 1). Among national samples of women in Ethiopia and men in Kenya, there appears to be a positive relation between education and HIV infection. However, by simply dividing the sample into rural and urban areas, this relation disappears. The change reflects the fact that HIV infection 7
expectancy coefficient from specification IV in Table 2. The standard errors for the Wald estimates are obtained from 500 bootstrap replications with the corresponding sample. In the pooled sample, the Wald estimates are 16% lower compared with the Mincerian estimates (7.90% vs. 9.40%). The Wald estimate is slightly higher for urban residents (10.21% vs. 9.50%), but it is lower for males (9.90% vs. 10.60%), females (4.30% vs. 7.30%), and rural residents (2.90% vs. 8.8%). For both Mincerian and Wald estimates, the return to schooling is always higher for males than females, and higher for urban than rural residents.

# **6.4 Robustness checks**

We reexamine our findings using several estimation methods and samples. We reestimated the models (i) without weights, (ii) using a different sample consisting only of data from one survey per country, (iii) using an alternative definition of parents’ life expectancy,<sup>19</sup> (iv) including cohort fixed effect with an alternative definition of cohort,<sup>20</sup> (v) using a sample consisting only of young age groups, and (vi) using a higher-order age variable to wipe out all age effects while estimating the life expectancy effects on earnings.<sup>21</sup> These robustness checks generate similar estimates of life expectancy effects on schooling and earnings. In most cases, the sign and statistical significance of the life expectancy coefficient is positive and statistically significant. The results for all robustness checks are briefly discussed and reported in Appendix C _._

> 19 Previously, parents’ life expectancy was constructed by taking a 25-year lag of life expectancy at birth. The youngest cohort in our survey was born in 1987. In the 1980s, in many regions, especially in Sub-Saharan Africa and South Asia, mother’s age at first child birth was less than 20. For example, in Niger half of the women had given birth by age 18 (Source: http://www.unicef.org/pon95/fami0009.html). An alternative measure of parents’ life expectancy assumed a 15-year lagged value of own life expectancy.

> 20 While constructing the five-year birth cohorts, we collapse all individuals aged 25-59 into different five-year birth cohorts, except for the first and last cohorts. In total, we define 13 cohorts based on 5-year birth groups. Since the number of observations before 1930 is too thin, we group those observations into one cohort. Similarly, all individuals who were born during 1985-87 were collapsed to form the youngest cohort.

> 21 In addition to those attempted for schooling, we try one additional robustness check for lifetime earnings. Following Card (1999) and Murphy and Welch (1990), we add higher-order age terms to sponge out all age effects.

22