rafmacalaba/datause-encoder-data huggingface dataset:
relatively large confidence intervals. These studies have large samples because they were part of large scale evaluations. This increases their influence in the total meta-analysis sample which might generate biases. However, these papers can also provide better precision in their estimates so that weights will compensate for both their relative sample size and their precision. Figure B2 shows a slightly different story. This figure shows the sources of effect size which come from publication bias.<sup>15</sup> The more papers are centered on unity, the more they are free of publication biases. For such papers, Meta-analysis procedures<sup>16</sup> allocate weights equal to zero. Other papers shifting slightly from unity and with large confidence intervals are imputed with very low weights. Papers with higher S.E. and relatively narrow confidence intervals, centered on unity, have the highest weights.

To corroborate the biases, Figures B3 and B4 in Annex 1 indicate which papers’ characteristics are producing publication biases by type of evaluation method and data used. Not surprisingly biases relied on a paper that used a structural model and three papers that used qualitative methods. These same papers used ad hoc surveys and other complementary data to conduct the analysis. The methodologies that don’t rely on robust estimates, and depend on calibrations or subjective criterion, produce publication biases by “using subjective or simulation methods where researchers adjust their models until the relationship between r and S.E. achieves some acceptable statistical significance” (Stanley, 2006).

Factors such as years of R&D program implementation, region and country of analysis, program targeting, and type of data contribute to isolate the program effect in terms of size and direction. Other paper-related characteristics are used to calibrate and adjust for such program effects and directions, given a paper’s quality, methodology, citations, identification strategy, etc.

Table 4. OLS Estimates

|OLS E|stimates Impact V|ariables||
|---|---|---|---|
|Dependent Variable|t-value of<br>Estimator|t-value of<br>Estimator|Study<br>Clti|
||(Impacts)|(Impacts)|orreaon|

> 15 Even figures B3 and B4 in Annex 1 show the same number of papers inducing publication biases depending on the method or data used.

> 16 These are computed using the metan, metabias and metacum stata commands in Stata.

23
# **Figure 2: Home-based work and face-to-face interactions do not always go hand-in-hand**

<!-- Start of picture text --> 0.90<br>Management of Professional, Scientific,<br>0.80 Education Services Companies and Technical Services<br>0.70 Finance and Insurance<br>Information<br>0.60<br>Wholesale Trade<br>0.50 Real Estate and Rental<br>and Leasing<br>0.40<br>Public Administration<br>Utilities<br>Arts, Entertainment,<br>0.30 and Recreation Other Services Administrative and Mining and Extraction<br>Support Services<br>of Natural Resources<br>Health Care and Social<br>0.20 Assistance Transportation and Manufacturing<br>Warehousing Construction<br>Retail Trade<br>0.10<br>Agriculture<br>Accommodation and<br>Food Services<br>0.00<br>0.00 0.20 0.40 0.60 0.80 1.00 1.20<br>More to less face-to-face<br>Less to moe home-based work<br><!-- End of picture text --> Source: Authors’ calculations based on Dingel and Neiman (2020), the O*NET database and the US. Bureau of Labor Statistics.

Among industries that are amenable to home-based work, the supply of those least intensive in faceto-face interactions, such as professional, scientific and technical services, might be least affected.<sup>6</sup> However, those intensive in face-to-face interactions are likely to experience negative productivity shocks during lockdown even if some tasks can be delivered digitally. For example, high school teachers can provide lectures through web-based applications, but the teaching quality might be lower compared to interactive sessions in the classroom. Similarly, branch managers and investment advisors in financial services can liaise with clients through online and telephone banking, but their ability to sell new products might be inhibited.

> cannot be done from home) but are likely to experience a surge in demand which can bias results. The scores for these observations can therefore be set to zero, to the minimum in the sample, or dropped. 6 The overall reduction in incomes and demand is just as applicable to these industries.

7
decompositions (exploiting the population additivity property of the aggregate poverty rate) show that almost one quarter of China’s poverty reduction over 1981-2001 can be attributed to population urbanization, even holding poverty measures constant in both urban and rural areas (Ravallion and Chen, 2007). By contrast, the urbanization process has brought very little gain to Africa’s poor, because it has not come with overall economic growth (Ravallion, et al., 2007).

Visitors to the Beijing Olympics might also be tempted to guess that the reasons for China’s success against poverty are found in the country’s booming, largely urban-based and export-oriented, manufacturing sector. Growth in China’s industrial sector has been impressive indeed, averaging about 12% per annum over 1985-2005. This has been fuelled by high levels of investment, notably foreign direct investment (FDI); Dollar (2007) argues that this also helped in diffusing new technologies, management skills and in establishing global production networks. In contrast to many countries in SSA, China’s policies have encouraged such foreign investment.

However, the boom in FDI was in the 1990s—after the bulk of the poverty reduction. Two-thirds of the decline in the number of people living under $1 a day over 1981-2004 occurred in the period 1981-87; an astonishing 40% occurred in just the first three years of that period (Chen and Ravallion, 2007). Yet 80% of the FDI in China during the period 1979-2005 was from 1995 onwards,<sup>18</sup> while only 15% of the decline in the number of poor (over 19812004) occurred after 1995. FDI was clearly not the “magic bullet” that reduced poverty in China.

A similar problem confronts another possible explanation: the expansion in China’s external trade—particularly in manufactured goods (World Bank, 2002). Trade reform begun in the early 1980s, with Deng Xiaoping’s “Open-Door Policy,” in the form exchange rate and tax concessions for exporters and creation of a special-economic zone, Shenzhen, near Hong Kong. Internal trade was also progressively liberalized. However, the bulk of the trade reforms did not happen in the early 1980s, when poverty was falling so rapidly, but were later, notably with the extension of the special-economic zone principle to the whole country (from 1986) and from the mid-1990s, in the lead up to China’s accession to the World Trade Organization (WTO).<sup>19</sup>

> 18 Furthermore, 98% of the FDI occurred after 1990. These calculations are based on the FDI (actual) series in $US provided in various issues of the China Statistical Yearbook (National Bureau of Statistics, various years).

> 19 Ravallion and Chen (2007) present evidence that mean tariff rates fell only slightly in the 1980s and nontariff barriers actually increased. Furthermore, some of the trade policies of this early period were unlikely to have been good for either equity or efficiency. For example, a two-tier price system allowed exporters to purchase commodities at a low planning price and then export them at a profit; thus oil was a major export item until 1986.

7
in each state.<sup>28</sup> A two-stage clustered design is assumed with clusters (defined above) serving as primary sampling units (PSUs) selected in the first stage within each domain and then a sample of 10 households within each cluster is selected in the second stage. With these design features established, the trimmed census data was analyzed to identify the parameter estimates for per capita income (mean and standard deviation) for each state and the target standard error implied by the parameter estimates that corresponds to an _RSE_ of 7.5 percent. The minimum sample size required for state _s_ , given these parameters, under simple random sampling (SRS) design is then obtained as: where _σs_ is the standard deviation of per capita income in state _s_ , _RSE_<sup>_tgt_</sup> is the target standard error of 7.5 percent in state _s_ , and _y_ ̄ _c_ is the mean per capita income in state _s_ . The minimum sample size under SRS must then be adjusted to account for the clustered design. This design effect due to clustering is accounted for by estimating the intra-cluster correlation ( _ICC_ ) for per capita income within each state using the trimmed census data. The _ICC_ estimates can then be applied to the SRS size obtained above to arrive at the minimum sample size for state _s_ under the clustered design employed here, given by: where _DEFFs_ is the design effect in state _s_ , _npsu_ is the number of households selected within each cluster (10 in this case), and _ρs_ is the _ICC_ for per capita income in state _s_ . The minimum number of clusters to achieve _ns_ (assuming 10 households per cluster) was calculated and then the final (household) sample size established by multiplying the number of clusters by 10.<sup>29</sup> Taking the sample size requirements from above as fixed, the sample in each simulation is selected in accordance with the two-stage design. PSUs within each state, referred to here as clusters, are selected with probability proportional to size ( _PPS_ ) without replacement, where the measure of size is the number of households within the cluster. Then 10 households were selected within each cluster via simple random sampling. According to this design, the inclusion probability for household _h_ in cluster _c_ and in state _s_ is approximated<sup>30</sup> as:

> 28The desired precision of an RSE of 7.5 percent is somewhat arbitrary but corresponds to precision targets in similar surveys and yields a sample of reasonable size.

> 29Full estimates and results for the sample size determination are available upon request.

> 30The equation used to calculate inclusion probabilities assumes sampling with replacement but is used here as an approximation of inclusion probabilities under PPS selection without replacement. This should provide a reasonable approximation in this case since there are a relatively large number of PSUs present in the frame.

17
# **Annex A: World Bank Enterprise Survey**

Table A1: Firm distribution and frequency World Bank Enterprise Survey Database by country

|**Country**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|Cambodia|845|5.30|5.30|
|China|2,700|16.93|22.23|
|Indonesia|2,763|17.32|39.55|
|Lao PDR|970|6.08|45.63|
|Malaysia|1,000|6.27|51.90|
|Mongolia|722|4.53|56.43|
|Myanmar|1,239|7.77|64.20|
|Philippines|2,661|16.68|80.88|
|Thailand|1,000|6.27|87.15|
|Vietnam|2,049|12.85|100.00|
|Total|15,949|100.00||

Source: World Bank Enterprise Survey Database. Note: Data is over the years 200‐2016 Table A2: Firm distribution and frequency World Bank Enterprise Survey Database by year

|**year**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|2009|4,184|26.23|26.23|
|2012|2,970|18.62|44.86|
|2013|832|5.22|50.07|
|2014|632|3.96|54.03|
|2015|4,651|29.16|83.20|
|2016|2,348|14.72|97.92|
|2018|332|2.08|100.00|
|Total|15,949|100.00||

Source: World Bank Enterprise Survey Database 31
# **Appendix A. About the Data**

The presidential speeches were obtained from the following sources:

**Argentina** : 1854-1868; 1872-1879; 1935-1940; 1942 ; 1946-1947; 1952-1955; 1958-1961; 1963-1966; 1973-1975; 1983-1994 (Provided by Chamber of Deputies); 1995-2001 **(** <u>Casa Rosada); 2002 (lavoz.com); 2003-2007 (Casa Rosada) 2008 (wikisource); 2009 (cfkargentina); 2010 (parlamentario/noticias); 2011 (cfkargentina); 2012 (elloropolitico);</u> 2013 (wikisource); 2014 (elloropolitico); 2015 (cfkargentina); 2016 (lanacion).

**Chile:** 1926-1929; 1932-1933; 1935-1936; 1939; 1941-1945; 1947; 1949; 1950-1953 1955. 1956; 1959; 1961; 1965; 1970-1976; 1980-2016; (Bibliotéca de Congreso Nacional)

**Colombia:** 1999-2001 <u>(ideaspaz.org); 2003 (Presidencia Colombia); 2004 (Portal Colombia.com); 2005 (historico.presidencia.gov.co/discursos/); 2006 (El tiempo); 2007 (alvarouribevelez.com.co);</u> 2008 <u>(elespectador.com);</u> 2009 <u>(eltiempo.com);</u> 2010 <u>(historico.presidencia.gov.co);</u> 2011 <u>(wsp.presidencia.gov.co);</u> 2012 <u>(e.exam10.com/ekonomika/13771/index.html);</u> 2013 <u>(wsp.presidencia.gov.co);</u> 2014 <u>(semana.com); 2015 (wp.presidencia.gov.co); 2016 (elheraldo.co).</u>

**Costa Rica:** 1878-1879; 1883-1892; 1894-1916; 1918-1922; 1924-1935; 1937-1947; 19492016. (https://sites.google.com/site/mensajepresidencialcr/).

**Dominican Republic:** 1999 <u>(leonelfernandez.com); 2000 (leonelfernandez.com; 2001 (Gobierno Hipolito); 2002 (Gobierno Hipolito); 2003 (Gobierno Hipolito); 2004 (Gobierno Hipolito); 2005 (diariolibre.com/); 2006 (leonelfernandez.com); 2007 (listindiario.com);</u> 2008 <u>(pld.org.do); 2009 (leonelfernandez.com); 2010 (leonelfernandez.com); 2011 (diariodominicano.com);</u> 2012 (elnacional.com.do); 2013 <u>(listindiario.com);</u> 2014 <u>(listindiario.com); 2015 (tirapiedras.com); 2016 (almomento.net).</u>

**Ecuador:** 1853; 1856-1858; 1861; 1863-1865; 1867; 1873; 1875; 1880; 1883; 1885; 18861888; 1890; 1892; 1894; 1898-1900; 1902-1924; 1928; 1932-1943; 1946; 1948-1949; 19511952; 1955; 1958-1962; 1966; 1968-1969; 1980-1983; 1985; 2002; 2006; 2008; 2010-2012; 2014-2016 (Provided by National Assembly of Ecuador).

**Mexico:** 1867-1871; 1873-1894; 1897-1903; 1905-1906; 1909; 1913-1914; 1917-1919; 1921-1936; 1938-1976; 1978-1989; 1993-2013; <u>(Bibliotéca Garay); 2014 (Gobierno de México); 2015 (Gobierno de México).</u>

**Paraguay:** 1881-1889; 1891; 1898-1900; 1902-1904; 1908-1909; 1917-1921; 1925-1926; 1928; 1934-1935; 1939; 1955-1958; 1960-1963; 1976-1977; 1980; 1982; 1986-1988; 1992. 1996; 1998-2014 (Provided by Congressional Library of Paraguay); 2016 (mre.gov.py)

**Peru:** 1829; 1832; 1834-1836; 1839-1840; 1845; 1847; 1849-1851; 1853; 1855; 1858-1860; 1862-1864; 1867; 1869-1870; 1872-1876; 1878-1879; 1881; 1883-1918; 1920-1928; 1930; . 1940-1961; 1963-1966; 1968- 2016 (Museo del Congreso)

27
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Figure 2 graphs regional data for education attainment at five-year intervals between 1970 and 2010. The figure shows that the MENA region was clustered at the low-stock end with South Asia and SubSaharan Africa in 1970 but diverged from this cluster over the next 40 years. By 2010, it had converged with the East Asia region and narrowed the gap with the Latin America and Caribbean region. In doing so, it experienced the fastest rate of stock growth among all regions over the past four decades.<sup>3</sup>

**Figure 2: Evolution of education attainment across regions, 1970-2010**

<!-- Start of picture text --> Average years of education<br>12<br>10<br>8<br>6<br>4<br>2<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Middle East & North Africa East Asia & Pacific<br>Europe & Central Asia Latin America & Caribbean<br>South Asia Sub-Saharan Africa<br>High income: OECD<br>Note: MENA region in dashed line<br><!-- End of picture text -->

# **D. Sources of Comparative Performance:  An Econometric Approach**

What accounts for the better comparative performance of the MENA region? From the general literature we know that such factors as income, public spending on education and measures of the opportunity cost of education are plausible determinants of the demand for and supply of education. When considering changes over time, it is important also to consider the initial level of education from which a country starts. The rationale for these factors is further discussed below.

_Initial level of education_ : It is natural to expect that, all other things being equal, a country that has a high level of education to begin with will find it difficult to increase this rate further while countries with lower rates will find it easier to do so. Indeed, for some measures of education, such as net primary or

> 3 Similar performance by the MENA region is also found for the period 1960-2000 by Glewwe and Kremer (2006) using an earlier version of the Barro-Lee dataset.

6
Table 13. Country level interactions

|Dep. Variables|TFP|log LP|LP Growth|
|---|---|---|---|
|Panel A||||
|FemHead|-0.044|-0.179***|-0.020***|
||(0.037)|(0.026)|(0.007)|
|FemHead* Discriminatory Family|-0.066<br>|0.043<br>|0.012<br>|
||(0.080)|(0.060)|(0.012)|
|Observations<br>|16,012|39,231|34,690|
|Adjusted R<sup>2</sup>|0.293|0.422|0.416|
|Panel B||||
|FemHead|-0.093*|-0.156***|-0.014**|
||(0.048)|(0.029)|(0.007)|
|FemHead* Restricted Physical Integrity|0.035|-0.039|-0.002|
||(0.071)|(0.063)|(0.014)|
|Observations|14,833|36,718|32,352|
|Adjusted R<sup>2</sup>|0.253|0.389|0.420|
|Panel C||||
|FemHead|-0.048|-0.097|-0.002|
||(0.046)|(0.076)|(0.013)|
|FemHead* High Literacy Rate|-0.049|-0.080|-0.018|
||(0.064)|(0.078)|(0.015)|
|Observations|19,730|49,748|44,493|
|Adjusted R<sup>2</sup>|0.260|0.372|0.373|
|Panel D||||
|FemHead|-0.036|-0.141**|-0.009|
||(0.043)|(0.069)|(0.016)|
|FemHead* High Gender equality|0.081|0.045|-0.005|
||(0.098)|(0.086)|(0.018)|
|Observations|5,235|15,164|13,042|
|AdjustedR<sup>2</sup>|0.153|0.308|0.493|
|Panel E||||
|FemHead|-0.161***|-0.217***|-0.023**|
||(0.051)|(0.048)|(0.011)|
|FemHead* Good Rule of Law|0.135**|0.108*|0.013|
||(0.063)|(0.061)|(0.013)|
|Observations|<br>20,157|<br>51,079|<br>45,711|
|AdjustedR<sup>2</sup>|0.283|0.389|0.372|
|Panel F||||
|FemHead|0.013|-0.039|-0.000|
||(0.036)|(0.053)|(0.010)|
|FemHead* High Individualism|-0.177***|-0.220***|-0.032**|
||(0.060)|(0.063)|(0.015)|
|Observations|17,281|41,142|37,483|
|AdjustedR<sup>2</sup>|0.284|0.399|0.387|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

All regressions have controlled for yearly, industry, country fixed effects and firm control variables which are same to baseline regressions in Table 5.

The country-level dummy variables used to construct the interaction term equals to 1 if the value of the original index above median, to 0 otherwise.

43
# 2.2. Outcomes

- <u>Caregiver perceived Intelligence</u>

We modeled our caregiver-perceived intelligence scale on the existing MacArthur Scale for Subjective Social Status used in adults and adolescents and measures an individual’s perceived economic status (Adler, et al., 2000). The MacArthur Scale has been shown to be an independent predictor of adult health (Singh-Manoux, Marmot, & Adler, 2005), adolescent self-rated health (Goodman, et al., 2007), and adolescent risky behaviors (Ritterman Weintraub, et al., 2015). In the present study, caregivers were asked to look at a picture of a ladder with rungs labeled from 1 to 7 and asked: “At the top of the ladder are the children with the best intelligence status and at the bottom are children with the worst intelligence status. In your opinion, where is your child on this scale?” Caregivers were asked to focus specifically on the target child of the study and rank the child in relation to other children in the community, using their own definition of intelligence. Prior to the survey administration, this scale was extensively pre-tested and the terms were discussed by investigators. The terms "Faharanitantsaina (intelligence, or having a ‘sharp mind’)" and "Maranitsaina (to be intelligent)” were selected and are commonly used terms in official Malagasy. In focus groups during the pre-testing, the term ‘intelligence’ was clearly perceived and understood by main caregivers as a broad concept of intelligence and child development that encompasses motor skills, problem solving, socio-emotional skills, language and cognitive capacity. In the instructions, interviewers were careful in asking caregivers to use their own definition of intelligence. Therefore, the subjective ranking was a function of individual and community characteristics.

<u>Developmental Abilities</u> 6
# _6.2. The effect of an increase in public investment on growth_

Graph 3 shows the effect of a permanent 1ppt GDP increase in public investment on growth. In the year following the shock, growth increases by the marginal product of measured public capital (equation 16A with θ�� �θ� ), which as argued above is around 0.17‐0.19ppts and is surprisingly similar across countries with different levels of development (blue dotted line with φ�0.17 , congestion specification ( ζ�1 )).

**<u>Graph 3: Incremental Output Growth from a 1 ppt. Increase in Public Investment</u>**

**in the LTGM‐PC (Congestion** , ζ�1 **)**

<!-- Start of picture text --> HI UMI<br>% %<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Standard LTGM Congestion, φ = 0.17 Standard LTGM Congestion, φ = 0.17<br>Congestion, φ = 0.10 Congestion, φ = 0.10<br>LMI LI<br>% %<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Standard LTGM Congestion, φ = 0.17 Standard LTGM Congestion, φ = 0.17<br>Congestion, φ = 0.10 Congestion, φ = 0.10<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br>2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> The second thing to note from Graph 3 is that the increment to growth falls over time. This is because as public capital accumulates, K<sup>��</sup> ⁄Y increases, which reduces the marginal product of measured public capital. This is intuitive: one would expect an increase in public investment to become less effective in boosting growth over time as “infrastructure needs” are met. There is some heterogeneity across income groups: the boost to growth is slightly less persistent in LI countries. This could be because these countries have a lower capital share ( 1 �β ), and so the marginal product of private capital which is “crowded in” declines more quickly (in addition to marginally higher K<sup>��</sup> ⁄Y which means the marginal product of public capital dwindles faster over time).

Finally, the effect of an increase in public investment in the LTGM‐PC (with φ�0.17 and ζ�1 ) is on average similar to the effect of a same‐sized increase in the aggregate investment in the Standard LTGM (red solid line) for all but HI countries. Specifically, the effect in the LTGM‐PC is very similar to the 24