rafmacalaba/datause-encoder-data huggingface dataset:
included in the specifications shown in Table 4. There is evidence that larger projects, in dollar terms, are less likely to result in satisfactory outcomes -- the variable _LNSIZE_ enters negatively and significantly in three of the four specifications, suggesting that project complexity (as proxied by size) is associated with worse project performance. There is also a very strong negative partial correlation between project length ( _PROJLENGTH_ ) and project outcomes, with longer projects associated with significantly worse outcomes in all four specifications. However, the interpretation of this partial correlation is complicated by the fact that we have information only on actual project length, not initially-planned project length. As a result, it is difficult to assign any causal interpretation to this correlation. It could simply be the case that some projects, especially the larger ones, “go wrong” and then take longer to complete than originally intended.

A similar concern clouds the interpretation of the negative partial correlation between project supervision costs ( _LNSUPERCOST_ ) and project outcomes. A plausible interpretation of this result is that when a project starts to go wrong, more resources are devoted to supervision in an effort to turn around the project. However, these efforts do not always succeed, and so the data show a negative partial correlation between supervision and project outcomes. Unfortunately, we do not have data on the distribution of supervision costs over the life of the project – given this information one could investigate whether more intensive early supervision is able to turn around problematic projects.

A much more surprising finding is the consistently negative partial correlation between project preparation costs ( _LNPREPCOST_ ) and project outcomes, though the results are not statistically significant at conventional levels. This indicates that on average eventual outcomes are less likely to be satisfactory in projects where more money is spent on preparation. This finding is particularly surprising because preparation costs are by definition incurred before the project begins. One interpretation of this finding is that initial costs signal “high-risk” projects, possibly with less recipient country ownership, that are (a) more likely to require intensive preparation and (b) more likely to ultimately receive unsatisfactory ratings. Finally, we include a set of five dummies indicating major project sectors (Agriculture and Rural Development, Energy and Mining, Transport, Education, and Economic Policy, with the omitted category consisting of an aggregate of the remaining sectors noted in Table 1). A strikingly consistent pattern is that projects in the Transport and Education sectors consistently perform better on average than projects in other sectors.

In Table 5, we introduce “real-time” project monitoring data from the ISRs, specifically the annual series of “problem” and “potential problem” flags over the life of each project. As discussed 16
compliance procedures. 4) High tax rates may lead to more corruption by increasing the incentive for taxpayers to evade them; however, there is no clear evidence to either validate or refute this (there is no clear support in the literature; for example, Ivanova, Keen, and Klemm, 2005). 5) Lack of sanctions is another important factor stimulating corruption. In the regression specification, tax simplification variables are included to capture Factors 1 and 2. Judicial determinants are included for Factor 5. We try to capture possible behavioral and cultural factors with political, economic and geographical determinants.

Based on the literature on corruption, the regression specification is defined as:

# Tax corruptionit

= α0 + α1. log (tax simplification)it + α2. economic determinantsit + α3. political determinantsit + α4. judicial determinantsit + α5. geograhical determinantsit +∈it In the regression specification for each set of determinants, different control variables are tried to see which ones can explain tax corruption best. Most of these control variables have already been introduced in the literature as possible determinants of general corruption in different countries. . Some papers investigating determinants of general corruption are listed below, while explaining control variables used in the regression analyses.<sup>7</sup> As possible **_economic determinants of corruption_** , the following variables are introduced in our regression analyses: index for wastefulness of government spending and global competitiveness index, both of which are from Global Competitiveness Index Database; real GDP per capita, real GDP growth rate, and the share of taxes in GDP, all of which are from the World Bank’s World Development Indicators. There are several empirical studies supporting the negative link between general corruption and market competitiveness.<sup>8</sup> Similarly, in the literature the negative link between the level of income and general corruption has been

> 7 Seldadyo and de Haan (2006) present a good literature review of empirical studies on corruption.

> 8 See, for example, Iwasakia and Suzukib (2012), Shabbir and Anwar (2007), Park (2003), Kunicova and Ackerman (2005), Gurgur and Shah (2005), and Graeff and Mehlkop (2003).

18
These are largely conjectures on my part. Rigorous testing requires some way of inferring the counterfactual distribution of impacts, in the absence of publication biases. Clearly this is difficult. However, there is at least one strand of evaluative research where publication bias is unlikely, namely replication studies that have compared NX results with experimental findings for the same programs (as in the meta-study for labor programs in developed countries by Glazerman et al. 2003). Comparing the distribution of published impact estimates from (nonreplication) NX studies with a counterfactual drawn from replication studies of the same type of programs could throw useful light on the extent of publication bias.

**_Can the lessons from an evaluation be scaled up?_** The context of an intervention often matters to its outcomes, thus confounding inferences for “scaling up” from an impact evaluation. Such “site effects” arise whenever aspects of a program’s setting (geographic or institutional) interact with the treatment.  The same program works well in one village but fail hopelessly in another.  For example, in studying Bangladesh’s _Food for Education_ Program, Galassso and Ravallion (2005) found that the program worked well in reaching the poor in some villages but not in others, even in relatively close proximity.  Site effects clearly make it difficult to draw valid inferences for scaling up and replication based on trials.

The local institutional context of an intervention is likely to be relevant to its impact. External validity concerns about impact evaluations can arise when certain institutions need to be present to even facilitate the experiments. For example, when randomized trials are tied to the activities of specific Non-Governmental Organizations (NGOs) as the facilitators, there is a concern that the same intervention at national scale may have a very different impact in places without the NGO. Making sure that the control group areas also have the NGO can help, but even then we cannot rule out interaction effects between the NGO’s activities and the intervention. In other words, the effect of the NGO may not be “additive” but “multiplicative,” such that the difference between measured outcomes for the treatment and control groups does not reveal the impact in the absence of the NGO.

A further external-validity concern is that, while partial equilibrium assumptions may be fine for a pilot, general equilibrium effects (sometimes called “feedback” or “macro” effects in the evaluation literature) can be important when it is scaled up nationally. For example, an estimate of the impact on schooling of a tuition subsidy based on a randomized trial may be 50
10 (10) where and V is assumed to be a normally distributed white noise error process. The model can then be consistently estimated by OLS.<sup>13</sup> Past micro-level housing demand studies have (universally it seems<sup>14</sup> ) used such single equations models. However, previous studies of foodgrain demands in this setting by one of the authors suggested that a degree of genuine price variability existed within our sample for foodstaples reflecting both spatial price differentials (due to imperfect market integration and transport costs) and seasonality (interviews having been spread over one year) (van de Walle 1988a). A single equations model is still, of course, appropriate if housing is separable from other goods in consumption. But we can see no good reason for believing that this is plausible.

So a second, more general, demand model is estimated which permits cross-price effects between housing and the main food-staple, rice. In this case the model becomes: where the cost-of-subsistence index now takes the form:
±0 Table 3: Skewness statistics for annual<sup>percentage change in RER for</sup> countries of the OECD and Southern<sup>Europe, the Franc Zone and two</sup> "dollar" countries, 1966-1988.

||b3|St|S<br>|b4<br>|SR<br>|
|---|---|---|---|---|---|
||Skewness|max-<br>mi|min /avg|kurtosis|std range|
|**OECD**<br>||||||
|Australia|-0.2?<br>|-0.03<br>|4.22<br>|-0.33<br>*|4.04<br>|
|Austria<br>|0.00<br>|-0.42<br>|2.81<br>|1.14<br>|4.47<br>|
|Belgium|-0.27|-0.28|2.37<br>|-0.83<br>|3.61<br>|
|Canada|0.03|0.21|2.00|-1.04<br>|3.59<br>|
|Switzerland|0.15|0.15|3.63<br>|1.75**<br>|5.23**<br>|
|Denmark|-0.28|-0.54<br>|2.34<br>|-0.97<br>|3.47<br>|
|France|0.11|0.59<br>|1.48<br>|-1.34**<br>|3.24<br>|
|Germany|0.15<br>|0.53<br>|1.68<br>|-0.87<br>|3.42<br>|
|Italy|0.08|0.25|2.55<br>|-0.12<br>|4.35<br>|
|Japan|0.08|0.50|1.90<br>|-0.92<br>|3.73<br>|
|Netherlands|1.25**|1.58**|2.37<br>|2.95**<br>|4.78**<br>|
|Norway|-0.33|-0.60<br>|2.36<br>|-0.92<br>|3.49<br>|
|Sweden|0.26<br>|0.45<br>|2.50<br>|0.25<br>|4.13<br>|
|United Kingdom|-<br>37|0.55<br>|1.7<br>|-0.58<br>|3.52<br>|
|UnitedStates<br>|_J.24_<br>|0.55<br>|1. 1<br>|-1.02<br>|3.55<br>|
|Median|0.08|0.25|2.37|-0.92|3.61|
|Franc Zone||||||
|Benin|0.62|0.89|2.07<br>|0.02<br>|4.01<br> <br>|
|Burkina Paso|0.17|-0.13|3.73<br>|1.52**<br>|<br>4.85**<br>|
|Cameroon|0.66|1.31*|1.87<br>|0.39<br>|4.12<br>|
|Central Africa|-0.20|0.28|3.04<br>|1.02<br>|4.90**<br> <br>|
|Congo|0.03<br>|-0.06<br>|3.79<br>|1.81**<br>|<br>5.21**<br>|
|Cote d'Ivoire|0.36|0.69|1.65<br>|-0.84<br>|3.41<br>|
|Mali|0.00|0.04<br>|2.61<br>|-0.29<br>|4.06<br>|
|Niger|-0.87**|-1.37*|3.56<br>|0.64<br>|4.02<br>|
|Senegal|1.22**|1.51**<br>|2.30<br>|2.16**<br>|4.54*<br>|
|Togo<br>|0.80*<br>|0.36<br>|2.49<br>|0.56<br>|3.93<br>|
|Median|0.27|0.32|2.55|0.60|4.09|
|S.<br>Europe||||||
|Greece|0.24|0.40|2.08<br>|-0.58<br>|3.79<br>|
|Hungary|-0.34|-0.58|3.00|-0.16<br>|4.09<br>|
|Portugai|-0.24|0.18|2.19<br>|-0.44<br>|3.59<br>|
|Yugoslav|-0.40|-0.92|3.14|0.02<br>|4.13<br>|
|<br>Median|-0.29|-0.75|2.14|-0.30|3.69|
|Dollar Countries||||||
|Liberia|-0.30<br>|-0.73<br>|2.81<br>|-0.55<br>|3.91<br>|
|Panama|-0.49|-1.72**|3.53|0.28|4.06<br>|
|Median|-0.40|-1.23|3.17|-0.14|3.99|

Note: ** (*) - significant at the 5 (10)% level.

6) _b4_ = /4 -3 Te2 The studentized range, which<sup>is the maximum less the minimum normalized by the</sup>
- 45 -

these countries. Women's participation iA the labor market is very restricted and concentrated in traditional fields. Accordingly, demand for higher education is also very limited due to factors such as family restrictions on female education. The eftective policies seem to be to increase parental demand for their daughters' educatioti. Provision single-sex schools in traditional fields seems to be the most effective strategy. In addition, community awareness campaigns identifying the benefits of female education may increase parental and hence female demand for post-secondary education.

_Upper Middle Income and Moderate Seting:_ This category enjoys the most gender parity in higher education for all categories. However, women may lag behind men in non-traditional fields in many of countries, such as Greece, Portugal, Korea, and much of LAC region countries. The main constraint here is not the demand for higher education, but the link between higher education that women receive and the labor market. Gender streaming at secondary level is the prime reason for women's inability to gain access to non-traditional fields. Also, in some LAC countries, legal restrictions do not permit women to accept jobs in some professions. Reform of the legal framework and greater emphasis on science and mathematics at the secondary school are critical for overcoming gender streaming in these countries.

# VI. CONCLUDING REMARKS

Bank project experience on what works to improve female access to tertiary education is so limited that it may be premature to draw firm conclusions. Many of the projects with interesting multiple interventions are currently ongoing. However, two conclusions emerge.

First, the key prerequisite for the observed success across countries of some interventions appears to be a strong demand for educated women in the labor market combined with a high private demand for higher education by women (and their parents). The degree of success of a project depended critically on the extent to which the project components were sensitive to the prevailing labor market situation for women and private demand conditions. Projects with often only a single intervention were successful in societies where the formal labor market is growing
economics literature to the drivers of inequality dynamics in countries other than the United States, Britain, France and Germany. In particular, studying cases where inequality actually fell for a sustained period of time can presumably teach us something about the combination of economic environments and policies under which such a trend is possible—even if, as in the case of rising disparities in the United States, there turn out to be many factors at play.

In this paper, we investigate the proximate determinants of a substantial decline in the inequality of labor incomes in Brazil—the largest economy in Latin America, and one of the 10 largest in the world—during the 1995-2012 period. Starting from very high levels, the Gini coefficient of the country’s distribution of household per capita income fell by 12 percent, from 0.59 in 1995 to 0.52 in 2012. A 7 basis points reduction in the Gini coefficient in 17 years is truly remarkable. For comparison, the Gini coefficient of household income inequality in the US increased by 8 basis points between 1967 and 2011, from 0.40 to 0.48 (Jacobson and Occhino, 2012). Most of the decline in inequality in Brazil can be attributed to changes in the distribution of labor earnings (Barros et al., 2010; Azevedo et al., 2013), and the Gini coefficient for that distribution fell by 18% in the same period, from 0.50 to 0.41.

Economic studies of changes in inequality fall into two broad categories: the first uses exogenous sources of variation (instruments or experiments, say) to identify the effect of one or two factors on the overall distribution (e.g., Autor, Dorn and Hanson, 2013). The second group of studies decomposes the overall change in the distribution into its statistical components (e.g., DiNardo, Fortin and Lemieux, 1996; Juhn, Murphy and Pierce, 1993). There are, of course, advantages and disadvantages to both approaches. In the former, a successful identification strategy would render us confident of the causal impact of the particular factor under consideration (e.g., the effect of trade with China on wages in certain parts of the United States), but the remainder of the change—including any offsetting effects—remains unidentified. In the latter approach, one obtains an exact statistical decomposition of the overall change, but the assumptions required for treating that decomposition as causal identification are somewhat stronger (see Fortin, Lemieux and Firpo, 2011).

We view these two approaches as complements, rather than substitutes. In this paper, we follow the second route and use recent decomposition methods based on re-centered influence function (RIF) regressions (Firpo, Fortin and Lemieux, 2009; and Fortin, Lemieux changes allows us to make inferences about labor demand that should be useful for students of labor market behavior generally” (Hamermesh, 2004: 554).

3
**Table A.5:** **<u>Construction of income aggregates</u>** ECAPOV harmonized data is use as a basis for construction income aggregates used in this paper. The main difference between income aggregate in this paper and the ECAPOV data stems from the definition of remittances. Remittances in this paper are defined differently between 2003 and 2007/2009 years given the changes in questionnaires.

In 2003 remittances include the transfers households received from donors residing outside of Tajikistan both in cash and in-kind forms (module 7a, questions 8 and 11). In 2007 and 2009 years migration module was expanded and remittances are defined as a sum of transfers from donors living outside of Tajikistan both in cash and in-kind (module 8a, questions 8 and 12), remittances from family members living abroad (module 2c, question 19), and finally as 70 percent of returned migrant net income while he/she was being abroad (module 2b, question 12).

Other income components include labor earnings, pensions, social assistance, and other income.

Labor earnings include wages and self-employment income. Pensions include old age, disability, social and survivor pensions.

Social assistance includes allowances, benefits to Chernobyl victims and Afghan war participants, illness benefits etc.

Other income include child benefits, unemployment benefits, property income (income from renting <u>property, income from selling assets and property), income from gambling, and interest rates.</u> 34
# **Table A4:  Asian Development Bank Country Policy and Institutional Assessments (ASD)**

|**Data Provider**<br>Asian Development Bank<br>||||||||
|---|---|---|---|---|---|---|---|
|**Description**<br>Multilateral development bank headqua|rtered i|n Mani|la, the P|hilippi|nes|||
|**Website**<br>www.adb.org||||||||
|**Data Source**<br>Country Policy and Institutional Assess|ments|||||||
|**Type**<br>Expert Assessment||||||||
|**Respondents**<br>Asian Development Bank country<br>|econo|mists,|subject|to|centraliz|ed re|view for|
|comparability||||||||
|**Frequency**<br>Annual since 2000||||||||
|**Coverage**<br>Asian Development Bank client countr|ies|||||||
|**Public Access**<br>Since 2005, only for countries eligible<br>Bank.|for co|ncessio|nal lend|ing by|the Asi|an De|velopment|
|**Description**<br>Indicators on 16 dimensions of policy<br>on a 6-point scale. CPIA indicators<br>Development Bank.|and in<br>are use|stitution<br>d to all|al perfo<br>ocate co|rmanc<br>ncessi|e. Resp<br>onal loa|onses<br>ns by|are coded<br>the Asian|
|2006<br>**Voice and Accountability**|2005|2004|2003|2002|2000|1998|1996|
|NA<br>..|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA<br>..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Competence of civil service<br>X|X|X|X|X|X|..|..|
|Budget Management<br>X|X|X|X|X|X|..|..|
|Efficiency of Public Expenditures<br>X|X|X|X|X|X|..|..|
|Management of public debt<br>X|X|X|X|X|X|..|..|
|**Regulatory Quality**||||||||
|<br>Trade policy<br>X|X|X|X|X|X|..|..|
|Competitive environment<br>X|X|X|X|X|X|..|..|
|Factor and products markets<br>..|..|X|X|X|X|..|..|
|**Rule of Law**||||||||
|Property rights<br>X|X|X|X|X|X|..|..|
|**Control of Corruption**||||||||
|<br>Anticorruption and Accounting Institutions<br>X|X|X|X|X|X|..|..|
|**Country Coverage**<br>**25**|**25**|**26**|**26**|**25**|**25**|**..**|**..**|
|**Year of Publication**<br>**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**..**|**..**|

42
# Effective InDuts

Previous developing country research on factors related to science achievement identified two inputs generally found effective in developing countries (textbooks and time) and one input with specific relevance to science teaching that has been found less effective (laboratories). The bulk of evidence regarding the effectiveness of these inputs comes from the first (1970-71) International Association for the Evaluation of Educational Achievement (IEA) science study, which included four developing countries (India, Thailand, Iran and Chile) and the Programa de Estudos Conjuntos de Integracao Economica da America Latina (ECIEL) survey of science achievement in Latin America (Bolivia, Brazil, Colombia, Mexico, Paraguay, Peru). A reanalysis of data from these studies found that school and classroom level variables accounted for significant proportions of studentlevel variance in science achievement in each of the countries (Heyneman and Loxley, 1983). Significant effects were found for time and instructional materials, but not for laboratory facilities.

Time. The amount of instructional time available for teachers and students has been found consistently related to achievement in both developed and developing countries. In developing countries, Heyneman and Loxley (1983) found several time use variables associated with science achievement: student time spent reading the science text in class (India, Iran, Thailand, Chile), time on homework (India, Thailand and Iran), and hours of science instruction (India, Thailand and Iran). Arriagada (1981, 1983), however, found conflicting results for teaching time in Colombia and 5