rafmacalaba/datause-encoder-data huggingface dataset:
Review of the Implementation Status of the Trans African Highways and the Missing Links. Rep. no. Volume 1. Stockholm: SWECO International AB, 2003.

Raleigh, Clionadh, et al. "Introducing ACLED: An armed conflict location and event dataset special data feature." Journal of peace Research 47.5 (2010): 651-660.

Reynal-Querol M (2002) Ethnicity, political systems, and civil wars. Journal of Conflict Resolution 46(1): 29–54.

Rutstein, Shea O. and Kiersten Johnson. 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, Maryland: ORC Macro.

Rutstein, Shea O. and Kiersten Johnson. 2004. The DHS Wealth Index. DHS Comparative Reports No. 6. Calverton, Maryland: ORC Macro.

Schneider G and Wiesehomeier N (2008) Rules that matter: Political institutions and the diversity– conflict nexus. Journal of Peace Research 45(2): 183–203.

Silverman, B. W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986. p. 76, equation 4.5 <http://books.google.com/books?id=exsrjsL7WkC&lpg=PP1&pg=PA76#v=onepage&q&f=false >.

Nepal. Working Paper, National University of Singapore.

Stifel, David, and Bart Minten. 2008. “Isolation and Agricultural Productivity.” Agricultural Economics, 39 (1): 1–15.

Ulimwengu, John, Funes, Jose, Headey, Derek and Liandzhu, You, 2009, “Paving the Way for Development? The Impact of Transport Infrastructure on Agricultural Production and Poverty Reduction in the Democratic Republic of Congo”, IFPRI Discussion Paper 00944. Verwimp, Philip. 2003. “Testing the Double-Genocide Thesis for Central and Southern Rwanda.” Journal of Conflict Resolution, 47(4): 423–42.

Stewart F (2002) Horizontal inequalities: A neglected dimension of development. QEH Working Paper Number 81. Queen Elizabeth House, University of Oxford.

UNEP (2011) “UNEP Study Confirms DR Congo’s Potential as Environmental Powerhouse but Warns of Critical Threats” <http://www.unep.org/newscentre/Default.aspx?DocumentID=2656&ArticleID=8890> Verwimp, Philip. 2005. “An Economic Profile of Peasant Perpetrators of Genocide: Micro-level Evidence from Rwanda.” Journal of Development Economics, 77(2): 297–323.

Warr, Peter, (2008), “How Road Improvement Reduces Poverty: The Case of Laos,” Agricultural Economics, 39, pp. 269-279.

29
_-4-_

# II. THU MODRL

# Consumption and Investment Opportunity Sets

6. It is assumed that the domestic individual can buy K commodities. These K commodities can be classified into three groups: importables, exportables and nontradables. The time path of the price P(i) of the ith commodity in terms of the domestic currency is described by a stochastic differential equation written as (1) dP(i) _V(st)p(i)dt_ + o(s,t) dZ , 2 where a is an Sxl vector of state variables; vp(i) is the instantaneous mean and ap(i) is the instantaneous variance of the pe-centage rate of change of the price of the ith good; and dZp(i) is a Wiener process. It is assumed that the individual takes the commodity priceb as given. by assumption the state variables follow Ito processes.3 The first K state variables in the vector a are taken to be the logarithms of the prices of the commodities available in the domestic country. Some of the other state variables will be specified later.

2 In the following, a vector will consistently be designated by a bold roman lower-case letter, whereas a matrix will be designated by a bold roman capital etter.

The properties of its processes and of the stochastice differential equations they follow are given in Merton (1971). See also Stulz (1981) (on which this part of the paper relies heavily) and Breeden (1979) fcr other references. rhe time paths of the prices of the commodities in terms of domestic currency can be related to the time paths of the prices of the commodities in terms of foreign currency (see further paragraph 8).
<!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>S 70%<br>—oO 60% TM Rudimentary*<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 2 40% TM Modern<br>4O 30% Informality*<br>S 20% TMFormality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>Box<br>£ 60% @ Rudimentary<br>S sox Informality<br><!-- End of picture text --> <!-- Start of picture text --> a 40% TM Modern<br>O 30% Informality<br>XS 20% © Formality<br>10%<br>0% :<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
3 policy developments. For example, Diao, Somwara and Roe (2001) and Hertel and Keeney (2006) draw on the GTAP database for 1995 and 2001, respectively. That GTAP database, which is updated every three years but typically with a long delay,<sup>1</sup> recognizes the ‘three pillars’ in the WTO agricultural negotiations (import tariffs, export subsidies and domestic production subsidies) but it tends to ignore export taxes, import subsidies and production taxes. While the latter set may have been relatively unimportant in 1995 or 2001, export taxes were re-introduced in Argentina at the end of 2001 and export restrictions were used by numerous developing countries when international food prices spiked upwards in 2008. Also, new evidence suggests changes in those latter measures, especially export taxes, are a significant part of the evolving global story of agricultural distortions over the past half century (Anderson 2009).

In the wake of this latest food price spike, and with the arrival of the new Distortions to Agricultural Incentives database compiled by the World Bank (Anderson and Valenzuela 2008) together with new methodological developments by Anderson and Neary (2005), it is timely to re-examine the relative contributions of different policy instruments to the trade- and welfare-reducing effects of agricultural policies across the world.

This paper makes two contributions over and above existing studies. First, it offers a new methodological approach for estimating the relative contributions of different policy instruments to trade and welfare reductions from agricultural policy. Scalar index numbers developed from the Anderson and Neary (2005) family of trade restrictiveness indexes are estimated for different policy instruments and then compared so as to show their relative contributions. Second, this study applies the methodology to the World Bank’s new dataset that allows for the estimation of the changing relative contributions over time of a comprehensive set of agricultural policy instruments to national, regional and global trade and welfare losses. The measures include all forms of border measures (import and export taxes and subsidies or the equivalent of non-tariff measures) as well as domestic production and consumption taxes and subsidies and farm input taxes and subsidies.

1 See, for example, Narayanan and Walmsley (2008) for the 2004 GTAP database.
Figure 2. Preference for Equality and Income Inequality <!-- Start of picture text --> 9.0<br>8.0<br>UKR<br>RUS<br>7.0<br>EST<br>MDA<br>6.0 BIHALB BGRPOL<br>CZE<br>BLR<br>5.0<br>LTU<br>4.0 SVNHRV<br>ROM<br>3.0<br>20 25 30 35 40 45 50 55 60<br>Gini Coefficient<br>Source: World Bank (2005) WDI database; WVS 1999-2001; and authors' calculations. Lower values of preference for equality indicate<br>greater preference for income equality.<br>Preference for Equality<br><!-- End of picture text --> However, underlying levels of income and inequality are not the whole story. Table 2 presents mean scores by quartiles and suggests that in addition to income and inequality there are distinct geographic patterns in preferences. The lowest quartile representing the greatest preference for equality is dominated by countries from Central and Eastern Europe. Preferences in this group are similar to more advanced economies such as Austria (4.6), Finland (4.6), and France (4.8). However, Lithuania, part of the former Soviet Union, also falls in this group. The top-most quartile reflecting the lowest preferences for equality is dominated by countries from the former Soviet Union (FSU) such as Estonia, Moldova, Ukraine and Russia. However, Bulgaria also falls in this group. The preferences of these countries are roughly comparable to those of low-income and middle-income developing countries such as Bangladesh (7.6), Peru (7.5), and Uganda (7.2). The exceptions notwithstanding, it would appear that if countries display a strong preference for equality they are likely to be from Eastern Europe, while if they display a low preference for equality they are likely to be from the former Soviet Union.

There are several reasons why countries in Central and Eastern Europe may be more inclined towards greater equality than countries that descended from the Soviet Union. First, this group of countries endured the economic disruption that ensued after the fall of the Berlin wall in 1989 and the end of communism relatively well. The decline in output was smaller for countries of Central and Eastern Europe than for countries of the former Soviet Union. Indeed, by the mid-1990s CEE had begun climbing out of the transition recession. By contrast, output decline was steeper and persisted longer in the former Soviet Union (World Bank 2002). This steeper and longer decline in the former Soviet Union may have resulted in more fraying of the social fabric, and a greater emphasis on self-preservation than among the citizens of the CEE.

8
Policy Research Working Paper 6247

# **Abstract**

This paper examines the long-term impacts of improved school quality at the elementary school stage on subsequent schooling investments and labor market outcomes using unique data from a recent survey that tracked students in the Philippines. The empirical results, which are based on a comparison of students who graduated from schools located in adjacent treatment and control areas before and after a school intervention, show significant differences in subsequent schooling investments, migration, and labor market earnings between females and males. That is, females study more (relative to males) and tend to migrate and earn more if they receive high-quality educational investments at an early stage. The above results are consistent with females’ greater incentives to study, driven by their higher returns to schooling, especially after high school completion, observed in the labor market.

This paper is a product of the Education Sector Unit, East Asia and the Pacific Region. 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://econ.worldbank.org. The author may be contacted at fyamauchi@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
where the dependent variable L (C C C C C C )j represents the log of the monthly value of crypto-assets transactions expressed in U.S. dollars for country _j_ at time _t_ -- note this is a flow rather than a stock concept. We use lagged values of country and global factors as market participants act according to the most recently available data which arrives with a lag in practice. The C C C C j −1 consists of a set of 1-month lagged macroeconomic variables for country CCLLC _j_ at time _t-1_ , including CCtt j −1 = CC jj −13−1 −1, the lagged monthly inflation rate percent CC change based on a comparison to the same month in the previous year for country ∆CCPPCC CC _j_ at time _t-1_ and ∆E j −1 = EE aa <u>jj</u> −1−2 −1, the lagged month-on-month percent change in the EEha RRaajjaa exchange rate vis-à-vis the U.S. dollar (i.e. an increase signifies currency depreciation) for country CChCCLLaaCC RRC CC EEha RRaajjaa _j_ at time _t-1_ . If crypto-assets are perceived as a hedge against domestic macroeconomic weakness, we expect crypto volume to exhibit a tendency to rise with a recent increase in domestic inflation or currency depreciation if investors expect such trends to continue in the future. Ideally, we would like to use measures that capture inflation expectations rather than realized inflation, but these indicators are not available for a broad range of countries. In robustness checks, we use changes in Broad Money instead of inflation, also calculated on a year-on-year basis. The C C C comprise a parsimonious set of 1-month lagged indicators. We use conditions in the U.S. as a proxy for global macro-financial conditions. jj−1 GGC GGC tt

- _Crypto-asset prices._ We include month-on-month price changes for two key crypto-assets: bitcoin and ether to explore the impact of prices on crypto volume. These price changes are calculated analogously to ∆E j −1.

- • _Gold prices._ Some major crypto-assets such as Bitcoin are perceived by some crypto-asset CChCCLLaaCC RRC CC

- _Gold prices._ Some major crypto-assets such as Bitcoin are perceived by some crypto-asset investors as “digital gold” or “digital stores of value” with macro hedge properties. Therefore, to explore the relationship with crypto volume and gauge whether the data bear out whether they are (imperfect) substitutes, we include month-on-month gold price changes.

- _Risk appetite_ . We use the VIX index, a U.S. stock market-based measure of investor uncertainty and risk aversion, as a proxy for global risk appetite. Higher values indicate higher levels of risk aversion. When risk appetite is high, investors may be more interested in risky investments such as crypto assets causing a rise crypto volume. The VIX index

25
instance a child grown up in a wealthy household, with parents employed in highly ranked jobs, might have been pushed to pursue a high profile career and, at same time, might have been used and educated to a healthy life-style with a vegetable-rich diet and regular sport practice, and thus in a position to reduce the probability of subsequent chronic conditions.

To address these empirical problems, we look at the siblings living in those households surveyed by the Egypt Household Health Utilization and Expenditure Survey (EHHUES) 2002, a cross section composed of about 33,000 observations. Siblings are more similar in terms of genetic endowment, preferences and family background than any two randomly matched individuals. Therefore any pair of siblings shares a common family fixed effect that we can control for by means of within-siblings estimates. Of course, especially among siblings of rather different ages, it is likely that an important idiosyncratic component remains uncontrolled for (Griliches, 1979), as family background itself might change overtime (Ermish and Francesconi, 2001). To avoid this problem, within-siblings estimators have been often applied to subsamples of twins (Ashenfelter and Krugman, 1994; Ashenfelter and Rouse, 1998), as twins share exactly the same family background on top of an almost identical genetic endowment. Unfortunately the number of twins is too small in our data to be of any use. Thus, to check the robustness of our identification strategy, we approximated the ideal case of twins by carrying out our analysis on the restricted set of siblings whose age differs by at most two or three years, obtaining results closely in line with those obtained from the unrestricted sample.

A second empirical issue that we have taken into account is the fact that the indicator of chronic diseases is self-reported and therefore subject to possibly relevant measurement errors (responsible for an attenuation bias in the estimates).

The remainder of the paper is organized as follows. Section 2 presents the theoretical framework from which the empirical models are derived. Section 3 describes which variables are included in the model. The identification strategy and a description of the data and the samples are discussed in Section 4. The main results are discussed in Section 5. Section 6 contains several robustness checks to inspect the validity of our identification strategy. Section 7 describes how the effects of chronic diseases vary depending on education, age, household income and gender. Finally, Section 8 concludes. Two technical appendixes contain further technical details.

# **The model**

An individual _i_ residing in household _j_ is endowed with the following utility function which summarizes his preferences over the set of work alternatives: 4
“treatment-no credit”: all DrumNet services except credit, totaling 377 individuals, and (3) “control”: no DrumNet services, totaling 367 individuals. Figure 1 presents a map of Gichugu with the location of the treatment and control SHGs.<sup>7</sup> After the randomization was done, we verified that the three groups were similar statistically on the limited variables available from the filter survey (i.e., number of members in 2004, SHG age since creation, access to paved road, percentage of members that were already growing export oriented crops, etc.). Table 1a reports these orthogonality checks. Column 4 reports the p-value of the t-test of the differences between the treatment group and the controls. Column 5 and 6 then show the breakdown for each of the two treatment groups, and column 7 reports the p-value of the F-test that neither coefficient for the two treatment groups is equal to zero. Although credit SHGs start off slightly worse than control SHGs in terms of infrastructure and remoteness, overall the three experimental groups seem quite similar. Note that in the analysis, since we have baseline data, we will include SHG fixed effects and all baseline controls of Table 1b. Thus any remaining differences in levels of fixed characteristics (but not trends in time-varying characteristics) that occurred due to the small sample will be controlled for through the SHG fixed effects and individual-level baseline control variables.

In April 2004, immediately after the filter survey was completed, we conducted a baseline of 726 farmers from the selected 36 SHGs. At the time of the baseline survey, DrumNet had not yet started operations or marketing, and thus no one had heard of it. During the follow-up survey in May 2005, we expanded the sample to include 391

> 7 Since the area is rather small, potential contamination of the control group is a concern. However, in the follow-up interview fewer than 15 percent of members in control SHGs had heard about DrumNet.

9
recreate. Second, since we are not valuing a change in the quality of the lake, we also avoid any quality change impacts on expected trip demand.

The household survey consisted of 3,358 households across Armenia, and the onsite survey of 389 tourists recreating at Lake Sevan. Travel cost models were constructed and estimated using travel expenditure and socio-demographic information contained in each survey. As visitation rates in the household survey contained a large percentage of zeros and the presence of over-dispersion in trip frequency, a zero-inflated negative binomial model (ZINB) was estimated. For the on-site survey, two truncated negative binomial models were estimated with and without an adjustment for endogenous stratification (ES).

Likelihood ratio tests for over-dispersion were rejected in favor of the negative binomial specification in both the household and on-site models. Results from the household model also reveal that the participation decision is indeed relevant to the household’s recreation decision. However, in the case of the on-site sample, estimated coefficients for the ES and non-ES models were not significantly different. This may suggest that characteristics from the on-site sample are representative of the household sample. Other studies have found similar results where accounting for ES did not yield any significant differences in trip demand or welfare (Ovaskainen _et al_ ., 2001; Englin _et al_ ., 2003). Per trip consumers surplus was estimated to be $8.82 for the household sample, $8.73 for the on-site model without ES adjustment, and $8.21 with ES.

The remainder of this paper is structured as follows. The next section provides a description of travel cost and count data models utilized in this study along with recommendations of how to remedy several dependent variable issues typically encountered with household and on-site recreational surveys. In Section III, the two surveys are described in more detail. In Section IV, the results of estimation are presented, along with a comparison in expected trip demand and estimated welfare measures. Section V provides a brief summary and discussion of the findings.

2
- Radermacher, W. J. (2021). Governing-by-the numbers–résumé after one and a half years. _Statistical Journal of the IAOS_ , _37_ , 701–711.

- <mark>Reddy, T., Nsubuga, R. N., Chirwa, T., Shkedy, Z., Mwangi, A., Awoke, A. T., ... & Janssen, P. (2023). Sustainable Statistical Capacity-Building for Africa: The Biostatistics Case.</mark> _<mark>Annual Review of Statistics and Its Application</mark>_ <mark>,</mark> _<mark>10</mark>_ <mark>, 97-117.</mark>

- Reporters without Borders. (2023). World Press Freedom Index. Available at <u>https://rsf.org/en/methodology-used-compiling-world-press-freedom-index2023?year=2023&data_type=general</u>

- Roseth, B., Reyes, A., & Amezaga, K. Y. (2019). The value of official statistics: Lessons from intergovernmental transfers. In _IDB Technical Note 1682_ . Inter-American Development Bank Washington, DC. http://dx.doi.org/10.18235/0001883

- Sachs, J., Kroll, C., Lafortune, G., Fuller, G., & Woelm, F. (2021). _Sustainable development report 2021_ . Cambridge University Press. https://doi.org/10.1017/9781009106559

- Sachs, J., Kroll, C., Lafortune, G., Fuller, G., & Drumm, E. (2023). _Sustainable development report 2023_ . Cambridge University Press. <u>https://doi.org/10.25546/102924</u>

- Sandefur, J., & Glassman, A. (2015). The political economy of bad data: Evidence from African survey and administrative statistics. _Journal of Development Studies_ , _51_ (2), 116–132.

- Sanga, D., Dosso, B., & Gui-Diby, S. (2011). Tracking progress towards statistical capacity building efforts: The African statistical development index. _International Statistical Review_ , _79_ (3), 303–329.

- <mark>Savoia, A., & Sen, K. (2015). Measurement, evolution, determinants, and consequences of state capacity: A review of recent research.</mark> _<mark>Journal of Economic Surveys</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(3), 441-458.</mark>

- <mark>Sumner, Andy, Eduardo Ortiz-Juarez, and Christopher Hoy. (2022). "Measuring global poverty before and during the pandemic: a political economy of overoptimism."</mark> _<mark>Third World Quarterly</mark>_ <mark>43, 1: 1-17.</mark>

- Tapsoba, S. J. ‐A., Noumon, C. N., & York, R. C. (2017). Can statistical capacity building help reduce procyclical fiscal policy? _Journal of International Development_ , _29_ (4), 407–430.

- Taylor, M. (2016). The political economy of statistical capacity: A theoretical approach. _Washington, DC: BID_ . <u>https://publications.iadb.org/en/political-economy-statistical-capacitytheoretical-approach</u>

- <mark>Tichenor, M. (2022). Statistical capacity development and the production of epistemic infrastructures.</mark> _<mark>Policy and Society</mark>_ <mark>,</mark> _<mark>41</mark>_ <mark>(4), 541-554.</mark>

- UNICEF. (2018). _Progress for every child in the SDG era_ . UNICEF New York. <u>https://www.unicef.org/reports/progress-every-child-sdg-era</u>

- United Nations. (2016). _Evaluation of the contribution of the united nations development system to strengthening national capacities for statistical analysis and data collection to support the_

27