rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Sample balance statistics

|||Mean[s.d.]||Difference|Number of|
|---|---|---|---|---|---|
||Control<br>(C)<br>(1)|<br>Treatment<br>(T)<br>(2)|Full<br>sample<br>(3)|(4)<br>(C-T)<br>[p-value]|(5)<br>obs.<br> <br>|
|Panel A: Individual characteristics||||||
|% male|0.42|0.49|0.45|0.20|420|
||[0.495]|[0.501]|[0.498]|||
|% with primary education|0.06|0.06|0.06|0.85|420|
||[0.242]|[0.233]|[0.236]|||
|% with secondary education|0.61|0.57|0.59|0.15|420|
||[0.488]|[0.497]|[0.492]|||
|% with tertiary education|0.32|0.38|0.35|0.17|420|
||[0.469]|[0.486]|[0.477]|||
|% young (18-25 year old)|0.23|0.20|0.21|0.34|420|
||[ 0.420]|[0.397]|[ 0.409]|||
|% young (18-29 year old)|0.31|0.25|0.28|0.11|420|
||[0.465]|[0.435]|[0.451]|||
|% young (18-34 year old)|0.41|0.35|0.38|0.16|420|
||[0.493]|[0.478]|[0.486]|||
|% currently employed|0.34|0.33|0.34|0.67|418|
||[0.476]|[0.471]|[0.473]|||
|Panel B: Household caracteristics||||||
|Per capita expenditure (JD per|63.02|63.37|63.19|0.90|406|
|month)|[44.67]|[44.810]|[44.682]|||
|% that has cars in the household|0.71|0.67|0.69|0.69|420|
||[0.691]|[0.832]|[0.763]|||
|Household size|6.07|6.11|6.09|0.84|416|
||[2.236]|[2.464]|[2.350]|||
|% with low ”subjective” income|0.18|0.23|0.21|0.15|412|
||[0.388]|[0.421]|[0.405]|||
|% with middle"subjective"income|0.783|0.751|0.77|0.38|412|
||[0.413]|[0 .433]|[0.423]|||
|% with high"subjective"income|0.03<br>[0.181]|0.02<br>[0.139]|0.03<br>[0.161]|0.48|412|
|Panel C: Giving behaviour||||||
|% that gave to charity in the last|0.65|0.58|0.62|0.14|413|
|3 months?|[0.477]|[0.495]|[0.487]|||
|% that can rely on kins’ help if|0.16|0.19|0.17|0.37|412|
|needed?|[0.366]|[0.394]|[0.380]|||

Notes: Columns 1 and 2 report the mean and standard deviation (in square brackets) of each variable for the control and treatment groups. Column 3 reports the mean and standard deviation (in square brackets) of each variable for the full sample (i.e. control + treatment groups). Column 4 reports the p-value of the t-test of the difference between the control and treatment groups (using clustered wild bootstrap-t statistics at the PSU level). Column 5 shows the number of observations used.

23
12

**Table 5.  The contribution of education to economic growth in the** **<u>early literature</u>**

|Country|Contribution of education to the<br>economic growth rate (%)|
|---|---|
|Belgium|14.0|
|Canada|25.0|
|Korea|16.0|
|UK|12.0|
|USA|15.0|

Source: Psacharopoulos (1984) Note: Estimates are based on within-country growth accounting using Schultz (1961b) and Denison (1967) methodologies The 1980s experienced the appearance of endogenous growth theory (Lucas 1988; Romer 1990). In their formulation, beyond a measure of human capital that is actually used by different firms in the economy ( _Kh_ ), _S_ years output also depends on the average level of human capital ( _K h_ ). In addition, human capital is endogenous, rather than exogenous, in the system; that is, human capital is produced by using resources: The dramatic theoretical implications of endogenous growth theories is that output is no longer constrained by the constant-returns-to-scale property of the Solow production

> function, and that ―knowledge‖ (proxied by _K h_ ) becomes a kind of public good that spills over the economy as an externality, allowing output to grow beyond the measurable inputs.  Another, equally important implication of this model is that, by virtue of the average stock of human capital being a public good, there might be social underinvestment in human capital formation.  This implication is very relevant to the education challenge topic of this paper.

Empirical applications of the new growth theory are relatively recent, diverse, and not easily summarized. Although some studies have reported a high impact of education on growth, others have argued that there is little or nothing to be learned from the empirical growth literature (for example, Topel 1999; Pritchett 2006; Banerjee and Dufflo 2005; Durlauf, Johnson and Temple 2005).

The inconsistency between micro and newer macro evidence on the returns to education has occupied the literature for a long time (for example, Krueger and Lindahl 2001; Sianesi and van Reenen 2003). The main reason for such inconsistency is that countries differ in many other respects that cannot easily be measured and controlled for in cross-country regressions, whereas within-country micro data automatically control for such factors.
# **Table A-1 (continued)**

# **<u>(iv) Guyana</u>**

|Variable label|Indo- Men|Afro- Men|Indo- Women|Afro- Women|
|---|---|---|---|---|
|Primary|0.05|0.21|0.41**|0.12|
||(0.08)|(0.14)|(0.14)|(0.24)|
|Secondary|0.21*|0.34*|0.71**|0.34|
||(0.10)|(0.15)|(0.16)|(0.24)|
|Tertiary|0.54**|0.68**|1.30**|0.71**|
||(0.15)|(0.16)|(0.32)|(0.25)|
|Age|0.09**|0.06**|0.04*|0.03|
||(0.01)|(0.01)|(0.02)|(0.01)|
|Age^2|-0.001**|-0.001**|-0.001*|0.000|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Employee|-0.03|0.02|0.03|0.31**|
||(0.06)|(0.06)|(0.10)|(0.08)|
|Georgetown|0.03|-0.03|0.53**|0.53**|
||(0.12)|(0.08)|(0.19)|(0.10)|
|Rural|-0.07|-0.22**|0.26|0.31**|
||(0.10)|(0.08)|(0.17)|(0.10)|
|Constant|8.34**|8.68**|7.89**|8.16**|
||(0.27)|(0.27)|(0.45)|(0.35)|
|Number of Observations|866|720|279|481|
|Adjusted R-squared|0.08|0.13|0.16|0.20|

Standard errors in parentheses; ** significant at 99% level; * significant at 95% level 36
policy perspective, interventions in the schools are generally viewed as both more acceptable and more likely to succeed than, say, direct interventions in the family.

Given the evidence on the importance of educational quality for economic outcomes, the study turns to important policy issues. To begin with, what can be said about the educational quality and cognitive skills in developing countries? Although information on enrollment and attainment has been fairly widely available, quality information has not. We use newly developed data on international comparisons of cognitive skills (also employed in the analysis of growth) to show that the education deficits in developing countries are larger than previously appreciated.

Discussions of quality inevitably lead to questions about whether it can be affected by policy. An extensive literature, albeit one biased toward developed countries, now exists on a number of policy issues. Perhaps most well known is that simply putting more resources into schools – pure spending, reduced class sizes, increased teacher training, and the like – will not reliably lead to improvements in student outcomes.

These findings are, however, often misinterpreted. First, they do not imply that schools have no effect. They say simply that common measures of school quality are in reality not closely related to student outcomes, but this is not the same as finding that school quality differences do not exist. Second, the findings do not say that spending and resources never matter. Indeed, there is some indication, particularly in developing countries, that a range of resources are important – textbooks, rudimentary facilities, and the like. The potential impacts of these are nonetheless too small to be instruments for radical changes in outcomes, something that the prior evidence indicates is needed in many developing countries. Third, the findings do not say that resources cannot matter. They indicate that resources may not have any consistent effects within the current structure and institutions of schools, but the findings do not put resource discussions into the context of alternative structures.

One consistent finding that is emerging from research, albeit largely from developed country experiences, is that teacher quality has powerful impacts on student outcomes. The problem from a policy aspect is, however, that quality differences are not closely related to the common measures of quality and to the common policy instruments that are employed. Within countries where the data exist, there is little indication that quality is closely related to teacher education and training, teacher experience, teacher certification, or teacher salaries. These facts disrupt the policy discussions. They also make it clear that different sets of policies must be contemplated if schools are to improve.

A different view of schools, however, concentrates on larger institutional issues. There is growing evidence that a number of devices – things that effectively change the existing incentives in schools – have an impact. Accountability systems based upon tests of student cognitive achievement can change 3
with fewer than five observations. Despite observing only six circumstance variables and exercising considerable parsimony in the partitioning of the population, we still have two surveys – from Guatemala and Panama – for which over 20% (40%) of cells have fewer than five observations in the income/consumption (earnings) analysis. By contrast, Brazil’s national PNAD survey, with a sample size one order of magnitude larger, has 6%-8% of cells with fewer than five observations. This reflects the limited sample sizes of LSMS surveys, and underscores the importance of the parametric estimates in validating (or refuting) the non-parametric results presented below.

Turning to the advantage variables, labor earnings are measured on an individual basis as monthly earnings from all occupations, including the monetary value of various in-kind payments. Family incomes and consumption are measured as per capita household income (from all sources) and per capita aggregate household consumption. Aggregates for family incomes are computed as the sum of all household members’ individual incomes, and include all jobs earnings plus other incomes such as those from assets, pensions and transfers.<sup>24</sup> Consumption expenditure data is not available for Brazil. Elsewhere, the reference period is the year, but some expenditures are captured on a weekly or monthly basis. Consumption aggregates do differ across surveys in some respects. In particular, income and consumption are adjusted for differences in the local cost of living in most LSMS datasets (Ecuador, Guatemala, and Panama but not Colombia) and in the Peruvian ENAHO dataset. LSMS surveys (Colombia, Ecuador, Guatemala and Panama) and the ENAHO survey also include imputed rents for owner-occupied housing in both consumption and income aggregates, whereas the PNAD does not. Table 3 (Panel B) reports means and standard deviations for these three economic advantage variables.

# **5. Inequality in Earnings Opportunity**

In most societies, remuneration for one’s work in the labor market is an important component of overall income, and thus a key determinant of a person’s command over

> 24 The reference period for the earnings of self-employed workers, which is the month in Brazil, Colombia, and Peru; depends on the frequency of payments in Panama; and is the year elsewhere. For wage earnings the reference period is the month in all surveys.

16
WPS 3474

# **Choosing Formulas for Market Access Negotiations: Efficiency and Market Access Considerations***

Joseph François _Tinbergen Institute and CEPR_ Will Martin _World Bank_ and Vlad Manole _World Bank_

## World Bank Policy Research Working Paper 3474, January 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We would like to thank numerous friends and colleagues, and particularly Elwyn GraingerJones, Bernard Hoekman, Richard Newfarmer, Marcelo Olarreaga, Ernie Preeg, and Alan Winters for valuable comments. This paper is forthcoming as Chapter 5 in Jayasuriya, S. (ed. _), Trade Policy Reforms and Development: Essays in Honour of Professor Peter Lloyd_ : Vol.2, Edward Elgar.

Correspondence addresses: J.F. François, Burg Oudlaan 50-H8-18, Erasmus University Rotterdam, 3000DR Rotterdam NL francois@few.eur.nl; Will Martin, World Bank, 1818 H St NW, Washington, DC 20433, USA. <u>wmartin1@worldbank.org;</u> Vlad Manole, World Bank, 1818 H St NW, Washington, DC 20433, USA. <u>vmanole@worldbank.org</u>
Table 7: Results with national economic condition indicators as control variables

|Dependent Variable|Hours Wor|kedper week|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|16.84|-4.744|-10.03*|1.326|
|1.pot<br>exp#c.indepvar|39.48*|6.124|-0.474|3.324***|
|2.pot<br>exp#c.indepvar|36.57|8.542*|1.150|3.359***|
|3.pot<br>exp#c.indepvar|44.60**|5.216|-2.523|2.705***|
|4.pot<br>exp#c.indepvar|31.40|4.094|-5.099|1.664|
|5.pot<br>exp#c.indepvar|37.86|2.767|-1.292|-0.289|
|6.pot<br>exp#c.indepvar|13.67|0.395|1.812|-2.570**|
|7.pot<br>exp#c.indepvar|-27.76|3.466|2.592|-2.398*|
|8.pot<br>exp#c.indepvar|52.96|5.806|3.883|-1.260|
|9.pot<br>exp#c.indepvar|-16.79|3.064|-1.564|-1.115|
|10.pot<br>~~e~~xp#c.indepvar|-91.94***|-1.493|-0.789|0.669|
|indepvar|2.331***|17.05***|37.30***|-2.427***|
|cohort|3.446***|1.160***|1.291***|0.748***|
|# obs|15,822|15,822|15,822|15,822|
|R-squared|0.671|0.528|0.681|0.507|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: (i) Hours worked per week for employed individuals are regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation.

(ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and *** refer to significance at the 10, 5 and 1% levels respectively.

19
We proceed by verifying the extent to which the results are sensitive to the de...nition of agricultural products we employ. In our baseline analysis, we rely on the UN statistics division of the SITC classi...cation. For robustness, in Table 4 we consider a broader de...nition that includes as well other agriculture-related products (Table A.3). The results reported in this table show that our ...ndings remain very similar when this alternative de...nition is used.

# [Table 4 about here]

Table 5 reports the estimates obtained when considering this broader de...nition of agricultural products and the restricted sample of countries that report at least four consecutive years of tari¤ and rainfall data. Once again, the results remain very similar.

# [Table 5 about here]

As mentioned above, a potential concern for our empirical analysis is that tari¤ setting might be constrained by limits associated with GATT/WTO membership. We adopt two alternative strategies to verify the extent to which our results are sensitive to tari¤ binding. First, we restrict the sample to include only country-product-year observations in which applied tari¤s are considerably smaller than the corresponding bound tari¤s. Second, we account for censoring by adopting a Tobit random e¤ects model on the full estimation sample.

Table 6 reports the results yielded by the ...rst of these strategies. While the analysis in the previous section spanned the 1988-2006 period, information on bound tari¤s is only available from 1995. Hence we begin by showing in column (1) that our baseline results remain qualitatively similar when we restrict the sample to include this sub-period only. In columns (2)-(4) we report results from analogous regressions, but now imposing progressively more stringent restrictions on the distance between applied and bound tari¤s. Reassuringly, we ...nd that the coe¢ cient of interest remains remarkably stable across all the subsamples considered.

# [Table 6 about here]

A potential concern with the results reported in Table 6 is that this approach introduces sample selection, which may a¤ect the estimated coe¢ cients directly. We therefore turn to the Tobit random e¤ects estimator. This method makes it possible to account for censoring due to tari¤ binding without introducing sample selection. A downside of the Tobit model, however, is that it is not possible to correct the standard errors for potential idiosyncratic correlation across products and over time within each country, implying that the standard errors are likely to be biased downwards. Table 7 reports the estimated marginal e¤ects. Columns (1) and (3) refer to models with country random-e¤ects (including 18
2 KENYA AND CHINA’S TRADE RELATIONSHIP higher spread on its sovereign bonds, the spread being already 600 basis points (6 percent) over US Treasuries (World Bank Africa’s Pulse 2015).

Kenya’s GDP will rebound, however, when China decreases investment and allows consumption to take a larger share of its GDP. Greater consumption usually leads to more imports, and demand for tourism, travel and business services will increase relative to commodities, an increase that should help Kenya’s service sector, an already strong sector (figure 13). From the boost from China’s greater consumption, Kenya’s GDP will 7.5 percent higher than it would have been if China stayed with seven percent growth. By 2030, the higher GDP should also bring higher wages and real exchange rate appreciation (World Bank 2015).

<!-- Start of picture text --> 5000<br>4000<br>3000<br>2000<br>1000<br>0<br>2009 2010 2011 2012 2013<br>Year<br>Export Value (USD millions)<br><!-- End of picture text --> Figure 13: Kenya’s services exports overall are strong Source: UN Service Trade 2015

**Threats to growth scenario: Strong Chinese downturn and terrorism** Kenya may not realize the benefits it China’s economy slows too much. A sharper downturn than expected will push down commodity prices even further, raising the cost of financing for Kenya; Kenya has a large current account deficit of ten percent of GDP (World Bank Africa’s Pulse 2015). Slower Chinese growth may also mean less FDI in Kenya; Kenya performs poorly in attracting FDI and inflows are volatile (see sections 3 and 4.1.1). Less FDI in Kenya may stall growth plans, plans that would upgrade much need infrastructure. Because China’s domestic consumption will shift toward final consumer goods, demand for natural resources and commodities will fall, and Kenya’s recently discovered oil will face lower global prices.

# **2.6.1 China’s rebalancing will help reduce poverty by 2030**

Lakatos _et al_ (2015) use a Global Income Distribution Dynamics (GIDD) model that incorporates micro and macro data for simulation of the effects of economy-wide changes in Sub-Saharan Africa. Changes in demographics, sectoral employment, per capita consumption growth, relative wages across sectors, and relative food to non-food prices are recorded by the model. It also uses 17
problem.<sup>22</sup> To minimize the bias, researchers should check the balance in the distribution of enumerator characteristics with respect to the treatment status when assigning enumerators to subjects.<sup>23</sup> Yet, balancing enumerator characteristics can be difficult to implement in the field. When this is not possible, at least researchers should balance enumerators themselves between treated and control groups.

In the case of surveys collecting sensitive information and for which there is not a clear identification strategy (for example Afrobarometer, World Bank Living Standard Measurement Survey (LSMS), etc.), the results suggest that these data need to be interpreted cautiously. Also in this case, simply including enumerator fixed effects in analysis is not enough. Data documentation for these surveys should provide, at a minimum, a clear description of how enumerators and teams were assigned. This would allow researchers to check for the presence and the size of the enumeration effect before conducting any analysis.

Note that, unlike other improvements in measurement, the suggestions from this study do not necessarily involve significant additional survey costs and does not generate tradeoffs between accuracy or bias and cost.<sup>24</sup> On the contrary, for most studies, limiting the possible bias from the enumerator effect can be done quickly, relatively easily, and inexpensively, and so represents a low-cost improvement in the quality of collected data.

> 22 In this case, controlling for enumerator dummies, would not prevent the enumerator effect from biasing the program impact. This bias could be even more problematic if combined with self-report data (e.g. see Barrera-Osorio et al. (2011) and Baird and Özler, (2012)).

> 23 For example, the study for this experiment – in which enumerators were randomly assigned - has an R2 for treatment status on observable and personality enumerator characteristics of 0.020 and on fixed effects of only 0.024. This indicates a minimal - if any – bias due to the enumerator effect.

> 24 Other strategies that could improve the quality of enumerator data collection include using alternative sources of data (i.e. administrative data) (Baird and Özler, 2012) and employing list experiments (see for instance Blair and Kosuke, 2012). Yet, the former it is not always a viable option while the latter does not allow for individual level analysis. Future work on how to incorporate private reporting of information, such as handing an electronic data collection device to a respondent to complete a question, could be fruitful.

20