rafmacalaba/datause-encoder-data huggingface dataset:
environment, strengthening regulatory agencies, increasing their independence, etc.). The policy challenge is to achieve a balance between effective regulation and increasing the contestability of markets. Much has been done by transition countries to reform and adapt policies and regulatory regimes for service industries. Figure 3 plots three indicators of the extent of policy reform in banking, non-bank financial services and infrastructure. These indices, constructed by the EBRD, range from 0 to 4.3, and span the period 1990-2004.<sup>4</sup> The value of the indices is set at zero for 1989, so that the 2004 value provides a measure of the progress that has been made by countries in converging to “best practice” standards—measured by a maximum value of 4.3. Data are available annually for the 1990-2004 period.

**Figure 3:  Services Reform Index, 2004**

<!-- Start of picture text --> 4.0<br>3.5<br>CEE<br>3.0<br>SEE<br>2.5<br>FSU1<br>2.0<br>FSU2<br>1.5<br>FSU3<br>1.0<br>FSU4<br>0.5<br>0.0<br>Banking Non-Banking Infrastructure<br>Services<br>Index<br><!-- End of picture text --> _Source:_ EBRD (2004). See Figure 1 for definition of country groups.

Central and East European (CEE) and Baltic states (FSU1) have made the most progress in all three services policy areas. For the other transition countries there is significant variation across indices. SEE has advanced the most on reforms in banking and infrastructure, followed by the Caucasus (FSU3), while European CIS countries (FSU2) have done the most in the non-bank financial area, followed by SEE. The Central Asian republics have made the least progress in all three areas, with one country—Turkmenistan—not advancing at all in any. What follows briefly discusses current policies for financial and infrastructure services.<sup>5</sup>

> 4 See Annex 1 for more detailed information on the construction of the EBRD reform indices.

> 5 What follows draws on a more detailed discussion in Eschenbach and Hoekman (2005).

7
Table 2: Relation between initial share of employment in mining and growth in per capita income (1996-2011)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.: Growth inper capita income(19|96-2011)||||
|Homeland (=1)|1.049**|1.152**|0.660|-0.032|
||[0.491]|[0.469]|[0.454]|[0.970]|
|Share Employed mining1996|-0.210|-0.004|-0.447*|-0.103|
||[0.215]|[0.247]|[0.258]|[0.226]|
|Homeland*Share Employed mining1996|0.641**|0.540*|0.840***|0.510**|
||[0.291]|[0.324]|[0.284]|[0.241]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.592|0.599|0.634|0.711|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
# AIDS and Dualism: Ethiopia’s Burden under Rational Expectations<sup>∗</sup>

Clive Bell<sup>†</sup> and Anastasios Koukoumelis<sup>‡</sup> First version: September, 2007 This version: March, 2009 _Keywords_ : HIV/AIDS, Growth, Dualism, Ethiopia _JEL Classification_ : I10, I20, O11, O41 ∗This paper – a product of the Global HIV/AIDS Program, Human Development Network – is part of a larger effort in the World Bank to assess the economic effects of HIV/AIDS, especially in Africa. An early version was presented at the Fifth Annual Meeting of the European Development Network in Paris, 2007. We thank the participants, especially Alice M ́enard, for valuable comments and suggestions, and we are also much indebted to Hans Gersbach for extensive discussions of all aspects of the study. Deepak Mishra provided a number of constructive comments on a recent version. We absolve all of them of any responsibility for all remaining errors of analysis and opinion. The views expressed in this paper do not necessarily reflect those of any of the institutions mentioned herein.

> †South Asia Institute, University of Heidelberg, Im Neuenheimer Feld 330, 69120 Heidelberg, Germany. Tel.: +49-6221-548824, Fax.: +49-6221-545596. e-mail: clive.bell@urz.uni-heidelberg.de

> ‡South Asia Institute, University of Heidelberg, Im Neuenheimer Feld 330, 69120 Heidelberg, Germany.

1
perceived by the beneficiaries. The fact that beneficiaries are encouraged by the policy to do something that they do not perceive as strategically relevant could be a positive result of the intervention assuming that policy-makers have a clearer and better understanding of the future perspectives and evolutions (Lukkonen, 2000).

# 3.1 Data and Methodology

The data used for this analysis was collected from papers that explicitly targeted evaluating R&D programs quantitatively.<sup>8</sup> Four categories of variables were collected from the papers and from the R&D programs. The first category of variables relate to the methodology and the estimation method used for the evaluation (which includes the type of data), and evaluation strategy used to identify impacts. The second category of variables relates to a paper’s attributes, including year and type of publication, region/country covered, and literature gap addressed among others. The third category of variables relates to R&D program attributes, containing the funds available, number of firms covered, types of institutions managing, and granting funds. Finally, the fourth category of variables relate to the quality of the publication proxy by citations, web references, and paper access statistics.

Differences among studies may be categorized broadly into those related to the phenomenon being studied and those unrelated. Choice of study design may induce differential biases in the results as well. As mentioned above, simple Ordinary Least Squares (OLS) will produce biased and inefficient estimators due to the implicit heterogeneity across papers and estimations. A meta-regression (MAE/MAR) can be either a linear or logistic regression model. In most metaregression approaches, the unit of analysis, that is each observation in the regression model, is a study. But studies differ in quality. This may lead to publication bias because poor quality studies were not selected as surveyed studies. However, there are methods to correct for publication biases using MARs -by allocating weights depending on the relevance and quality of the publication (see figures B1-B4 in Annex 1 for publication biases in our database), among other attributes. The questions that a meta-analyst may answer with a meta-regression include

> 8 Two qualitative studies that used survey data from firms were included.

11
about private savings, but not the decision of whether to place the private savings into a tax-deferred pension account.

The model is dynamic and explicitly incorporates forward-looking behavior under a rational expectations assumption. It also incorporates uncertainty and incomplete information. Specifically, individuals are uncertain about future wage shocks, future fertility, future divorce or widowhood, future survival and investment returns at the time of making labor supply and savings decisions. In solving the model, we assume that the 2008 pension reform came as a surprise and was not anticipated. We assume this, in part, because our discussions with the Budget Office in Chile indicated that the reform was not anticipated.<sup>34</sup> Thus, decisions up until 2008 are governed by a pre-reform decision model and decisions after 2009 are governed by a post-reform model. This requires solving two different versions of the model. First, we solve the dynamic programming problem (obtain the Emax values) assuming no reform and we estimate the structural model parameters only using pre-pension reform data. Then, fixing those parameters, we resolve the dynamic programming problem (i.e. obtain the Emax values that individuals use to form expectations) with the reform in place. These new Emax values are then used to simulate decision-making after the reform was introduced.

To a limited extent, the model incorporates business cycle effects in that returns on pension investments vary over time. Two limitations of the model are that investment returns are assumed to be i.i.d. and that there are otherwise no aggregate earnings shocks. However, aggregate demographic changes in the economy are incorporated in a few ways. First, the initial conditions include the education levels of the husband and wife and rising levels of education with successive birth cohorts will lead to different decision-making for different cohorts. For example, more recent cohorts of women who have higher education levels on average and the model will generate that they have fewer children and participate more in the labor force. Also, the model takes marital sorting patterns as initial conditions, so any changes over time in marital sorting patterns can also generate differences in behaviors across birth cohorts.

34Modeling the reform as anticipated would have also been feasible, but would require somewhat arbitrary assumptions about when the details of the reform became known to workers.

19
Table 6. Treatment Effects on Empathy and Perspective Taking

||Empathy<br>scale<br>Per<br>(1)|spective-taking<br>scale<br>(2)|
|---|---|---|
|_Panel A. Treatment Effects_|||
|Game [=1]|-0.050|0.181**|
||(0.078)|(0.080)|
||[0.306]|[0.110]|
|Video [=1]|0.117|0.114|
||(0.081)|(0.084)|
||[0.213]|[0.213]|
|Observations|828|828|
|Av. Dep. Var. (Control group)|0.000|0.000|
|Diff. p-value|0.033|0.393|
|YWYjoint test|.271|.041|
|_Panel B. Treatment Effects by Dur_|_ation in Minutes_||
|Game Duration|-0.0004|0.006**|
||(0.003)|(0.002)|
||[0.420]|[0.101]|
|Video Duration|0.022*|0.015|
||(0.012)|(0.013)|
||[0.140]|[0.196]|
|Observations|828|828|
|Av. Dep. Var. (Control group)|0.000|0.000|
|Diff. p-value|0.054|0.397|
|YWYjoint test|0.150|0.053|

_Notes:_ This table displays OLS estimates derived from Equation 1. Dependent variables are constructed using the scales of the Interpersonal Reactivity Index (IRI) originally formulated by (Davis, 1980, 1983). (i) The Empathy scale consists of eight statements assessing the tendency to feel sympathy and compassion for others in distress. (ii) The Perspective-taking scale comprises seven statements measuring the inclination to adopt others’ psychological viewpoints in daily life. Respondents rate each of the 15 statements on a scale from 0 to 4, where 0 indicates “does not describe me well” and 4 indicates “describes me very well.” Scores range from 0 to 4 for each statement, with the total score being the sum of partial scores. All regressions control for gender and student status, the stratification variables. Robust standard errors are reported in parentheses, with False Discovery Rate (FDR) q-values in brackets. Av. Dep. Var. (Control grp.) represents the mean of the dependent variable in the control group. The Diff. p-value is a Wald test of equality of coefficients between the game treatment and video treatment. YWY denotes the sharpened FDR q-values. The Westfall-Young joint test of the sharp hypothesis, asserting that no treatment affects the outcomes, yields a p-value of 0.070. Significance levels are indicated as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.
**Table 9: Number of workers   in presumed comparative advantage industries by enterprise age group, UBR 2001 and UBR 2010**

||**_A. All comparative adva_**|**_ntage industr_**|**_ies_**|||**_E. Commerc_**<br>**_farming_**|**_ial_**||||
|---|---|---|---|---|---|---|---|---|---|---|
|||||||||% share in|sector||
|**Age Group**|Number of workers||% share in sec|tor total|Growth|Number o|f workers|total||Growth|
|**(years)**|2001|2010|2001|2010|(%pa)|2001|2010|2001|2010|(%pa)|
|< 5|15,739|35,510|28.9|33.0|14.0|3,579|4,445|28.9|33.0|2.69|
|5 to 10|16,857|18,348|30.9|17.0|1.0|11,340|16,208|30.9|17.0|4.77|
|More than 10|21,888|53,827|40.2|50.0|16.2||66|40.2|50.0||
|Total|54,484|107,685|100.0|100.0|10.8|14,919|20,719|100.0|100.0|4.3|
||**_B. Comparative advanta_**|**_geprimary i_**|**_ndustries_**|||**_F. Food and_**|**_beverages_**|**_industry_**<br>|||
|||||||||% share in|sector||
|**Age Group**|Number of workers||% share in sec|tor total|Growth|Number o|f workers|total||Growth|
|**(years)**|2001|2010|2001|2010|(%pa)|2001|2010|2001|2010|(%pa)|
|<5|5,012|12,447|29.2|29.2|16.5|11,674|5,061|24.3|29.2|‐6.3|
|5 to 10|3,929|7,789|19.0|18.3|10.9|8,792|20,910|19.0|18.3|15.3|
|More than 10|11,692|22,418|56.7|52.6|10.2|28||56.7|52.6||
|Total|20,633|42,654|100.0|100.0|11.9|20,494|25,971|100.0|100.0|3.0|
||**_C. Comparative advanta_**<br>**_industries_**|**_ge manufact_**|**_uring_**|||**G. Thegarm**|**ents indust**|**ry**|||
|**A G**||||||||% share in|sector||
|**ge roup**|Number of workers||% share in sec|tor total|Growth|Number o|f workers|total||Growth|
|**(years)**|2001|2010|2001|2010|(%pa)|2001|2010|2001|2010|(%pa)|
|<5|9,959|21,353|35.5|35.5|12.7|582|3,420|16.6|35.5|54.2|
|5 to 10|12,596|9,347|39.0|15.6|‐2.9|656|6,594|39.0|15.6|100.6|
|More than 10|9,703|29,384|30.1|48.9|22.5|74|387|30.1|48.9|47.0|
|Total|32,258|60,084|100.0|100.0|9.6|1,312|10,401|100.0|100.0|77.|
||**_D. Comparative advanta_**|**_ge services_**||||**H. Land tra**|**nsport servi**|**ces**|||
|||||||||% share in|sector||
|**Age Group**|Number of workers||% share in sec|tor total|Growth|Number o|f workers|total||Growth|
|**(years)**|2001|2010|2001|2010|(%pa)|2001|2010|2001|2010|(%pa)|
|<5|768|1,710|34.6|34.6|13.6|295|853|15.5|34.6|21.0|
|5 to 10|332|1,212|20.8|24.5|29.5|418|1,661|20.8|24.5|33.0|
|More than 10|493|2,025|30.9|40.9|34.5|66|129|30.9|40.9|10.6|
|Total|1,593|4,947|100.0|100.0|23.4|779|2,643|100.0|100.0|26.|

43
16 (8) An Industry Scale Variable - Identifies industries where economies of scale appear to be operative (see the notes to Table 3). Is developing countries comparative advantage adversely affected by larger scale production requirements.

These variables were then matched with the labor intensity and revealed comparstive advantage indices at the level of product detail indicated in Appendix Table l.'4 Correlations were then run with these explanatory variables and the RCA indices.

Table 3 shows Spearman correlation coefficients for these explanatory variables and the industry RCA values for all developing countries as well as those for the two groups of Asian countries. In all three country groups the labor intensity variable is statistically significant at the 99 per cent level and takes the expected (negative) sign.15 The human capital (per capita wage) and physical capital variables are also highly correlated with the developing countries' revealed comparative advantage and the signs associated with these variables are as expected -- as physical and human capital requirements increase 14 The reader should note that the NBER measure (equation 1) represents capital intensity that combires physical and human capital. Variables (1) and (3) above attempt to independently measure the effects of human and physical capital. The human capital, physical capital and product differentiation variables are all based on U.S. Department of Commerce, Census of Manufactures data, while the market size variable was estimated using United Nations, Series D Commodity Trade Statistics. UNIDO (1982) was the source of resource based product information, while Hufbauer (1970) identified the first trade dates for the different products. In some cases, these dependent variables were derived for three-digit product groups and it was necessary to apply these values to underlying four and five-digit products listed in Appendix Table 1. See Balassa (1979, pp. 260-262) for suggestions of other variables that might be tested, as well as a discussion of the use of stock and flow variables for the capital measures.

's The negative sign was expected since an inverse indicator is involved - that is, the higher the value of the index the less labor intensive (i.e., more capital intensive) is the production process. As such, Table 3 shows that, as one moves to less labor intensive production processes developing countries' revealed comparative advantage indices decline.
- Sen, A. and Himanshu (2004), ―Poverty and Inequality in India-II: Widening Disparities during the 1990s‖, _Economic & Political Weekly,_ 39

Solon, Gary (1999). ―Intergenerational Mobility in the Labor Market,‖ in O. Ashenfelter and D.

- Card (eds.), _Handbook of Labor Economics_ 3A, Elsevier, Amsterdam, North Holland.

- Solon, G, M. Page, and G. Duncan (2000). ―Correlations between Neighboring Children in Their Subsequent Educational Attainment,‖ _Review of Economics and Statistics_ 82(3), 383-392.

- Solon, G, M. Corcoran, R. Gordon and D. Laren. 1991. ―A Longitudinal Study of Sibling Correlations in Economic Status,‖ _Journal of Human Resources_ 26 (3), 509-534.

<mark>Srivastava, S (2004), ―India mega-wedding justifies hype‖, BBC Online, February 11, 2004.</mark>

- <mark>Strauss, John and Thomas, Duncan, 1995. "Human resources: Empirical modeling of household and family decisions," Handbook of Development Economics, in: Hollis Chenery† & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 3, chapter 34, pages 1883-2023 Elsevier.</mark>

<mark>Woolsey, M (2008), W</mark> orld's Most Expensive Homes: Inside the World's First Billion-Dollar

- Home‖, Forbes Com, April 30, 2008.

- Walton, M (2010). Inequality, Rents and the Long-run Transformation of India, Working Paper, KSG, Harvard University.

- World Bank (2011). _Perspectives on Poverty in India: Stylized Facts from Survey Data_ , World Bank, Washington DC.

36
**Table** 17: **Conflict and complementarity in stakeholder interests**

||**GOO**|**Tehsil-level**<br>**revenue**<br>**bureaucracy**|**NGOs**|**Large**<br>**landowners**|**Small and**<br>**marginal**<br>**farmers**<br>|**Landless**<br>**farmers**|**Women**|**Tribals**|
|---|---|---|---|---|---|---|---|---|
|**GOO**<br>i|_||**Y**|*|*|-|||
|**NGOs**||*|V<br>||*|-|||
|**Large____**<br>**landowners**|||||||||
|**Small and**<br>**marginal**<br>**farmers**|||||||||
|**Landless****<br>**farmers__**||||__|*||||
|**Women**|||||||||
|**Tribals**|**+_**||||||||

> Notes: Conflicts of interest are represented by *, complementarities of interest by **v.** The size of the symbol represents the extent of the conflict or complementarity. The shaded areas indicate the probable sources of resistance to policy change.

67
|Type data an Method||||
|---|---|---|---|
|<br>Survey|||-0.0281|
|<br>s.e.|||(0.03)|
|Panel|||<br>0.0179|
|s.e.|||(0.03)|
|Cross-section|||<br>0.0521|
|s.e.|||(0.04)|
|Region ECA||1.168|<br>0.0853**|
|<br>s.e.||(0.95)|(0.03)|
|Region LAC||<br>1.131**|<br>0.0917**|
|<br>s.e.||(0.45)|(0.04)|
|Region EU||<br>2.746|<br>0.117**|
|<br>s.e.||(1.71)|(0.04)|
|Region North||||
|<br>America/Other||6.09|0.107*|
|<br>s.e.||(5.24)|(0.05)|
|Late Stage of Program|||<br>0.0281|
|<br>s.e.|||(0.03)|
|Quasiexperimental method||||
|used|||-0.0436|
|s.e.|||(0.03)|
|Complementarity|||<br>0.0508**|
|<br>s.e.|||(0.02)|
|Paper precision|0.118**|0.109*||
|<br>s.e.|(0.06)|(0.06)||
|Constant|<br>2.284***||-0.0707|
|s.e.|(0.79)||(0.05)|
|Observations|<br>37|37|<br>29|
|R-squared|0.158|0.45|0.429|
|Robust standard errors in p|arentheses|||
|*** p<0.01, ** p<0.05, *||||
|p<0.1||||

Once meta-graphs identify the papers that produce bias and assign weights to each paper, the WLS results show that the precision estimator is close to zero and the average R&D effect estimator from all paper estimates is 0.19. Results also show that by eliminating biases, the EU and North American papers showed highest precision, compared to the rest of the regions. Quasi-experimental studies with sample sizes above the average also showed the highest precision compared to other methods and sample sizes. In the area of R&D experimental evaluation designs are rare, so the second-best option for researchers is to rely on existing data to evaluate by statistically building a counterfactual.

24
Policy Research Working Paper 7442

# **Abstract**

An export-oriented development strategy fostered the Asia Pacific region’s economic success, making it the fastest growing region in the world. In recent years, despite waning demand from the crisis-hit Western economies, the accelerating demand from China boosted intraregional trade in Asia. Although China’s Asian trading partners benefit from increasing exports to China, this stronger linkage with China has made them more vulnerable to the risk of a Chinese slowdown. This paper examines the impact of a negative Chinese gross domestic product (GDP) shock on Asian economies by employing the Global Vector Autoregressive (GVAR) model, using the dataset through the third quarter of 2014 for 33 countries. The analysis finds that a negative Chinese GDP shock impacts commodity exporters, such as Indonesia, to the greatest extent, reflecting both demand and terms of trade shocks. Export-dependent countries in the East Asian production cycle, such as Japan, Malaysia, Singapore and Thailand, are also severely affected. The analysis also finds that a negative shock to China’s real GDP would not only have an adverse effect on the price of crude oil, as some previous studies have also shown, but also on the prices of metals and agricultural products. The study also investigates the impact of a potential negative shock to the real GDP of the United States on Asian countries, and determines that although the U.S. economy has a larger influence on Asian economies than China’s economy, the Asian countries are more exposed to China than ever through increased economic ties.

This paper is a product of the IDA Resource Mobilization Unit, Development Finance Vice Presidency. 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 authors may be contacted at dkaya@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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