rafmacalaba/datause-encoder-data huggingface dataset:
Unadjusted and adjusted changes in risk associated with pre-natal and post-natal risk factors for infant and early childhood stunting (6-23 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Low birth weight (<2,500g) | = —92_ a<br>Mother has short stature (height < 145cm)} 9 ro —<br>Mother has low body mass index (BMI < 18.5) $ 144 _ —44_<br>Attended at least 4 antenatal visits 7 2 —+— ——_<br>Skilled birth delivery | = ee ee<br>Child is currently breastfed | ® —— 3<br>Child is currently fed solid or semi-solid foods 9 —-——- ———_<br>Child is currently fed other milk & oe oe<br>Mother reads & plays with child] <= —— ——<br>Mother handwashes regularly 12 —— ——<br>Household is food secure |” —- —<br>HH consumed protein rich foods in past week] ? 1°<br>HH consumed vitamin A rich foods in past week | & _ ~~<br>Underwent neonatal screening 2 —— ——<br>SkillAg e d post-natappropri a lte carevaccine for “restatus; .= ao— a_<br>Improved drinking water 4%" ——- a<br>Improved sanitation (toilet is water sealed) | 7 ——- ———<br>>75% of HHs in municipality have improved sanitation s. —— ——<br>Improved cooking fuel & no garbage burn} $ —— ——-<br>HH member smokesj 3 ee —<br>Functioning refrigerator in household} 3 oe Ost<br>Female oa ope<br>Single birth} © —2#—— 2<br>Urban ——<br>Total HH size (N)j a +<br>Total children under five in HH (N) ° 43<br>Total HH income per month (1 = 1000 PHP) (log) s ——<br>In poorest quintile of total HH income per capita] 6 a<br>Mother has high school or tertiary education} % ——<br>Mother is married —+—<br>Mom wasa teenager at time of birth —_—+—<br>Mother hasa job or business Te<br>0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. The unadjusted regression model does not control for socio-economic factors.
round of ENOE, respondents are interviewed every quarter for a total of five quarters,<sup>2</sup> with a fifth of the sample being refreshed every quarter.

In line with Bobba, Flabbi, and Levy (2022), we rely on the 2005 round. This is due to two reasons: first, informality rates remained relatively stable over this time period and second, the institutional background was likewise stable, with no reform taking place in the period. Similar to our treatment of ELMPS, we restrict our sample in ENOE to individuals aged between 20 to 53 at the beginning of the survey whenever relying on the panel dimension of the data. As in the ELMPS, we choose not to impose restrictions on sector, rurality, work schedules or education levels to capture a more comprehensive picture of the two labor markets, and thus avoid overrepresenting certain sectors or labor market states over others.

The definitions of labor market states used in ENOE are consistent with those in ELMPS. Unemployment requires job search effort and absence of a job or market activity in a one-week reference period. Being out of the labor force requires an individual to not have conducted search in the reference week.<sup>3</sup> The ENOE contains a detailed discussion on the definition of formality and informality as part of its documentation,<sup>4</sup> consisting of 14 categories for the former and 9 for the latter. Similar to the ELMPS, all workers without social security benefits are classified as informal. One category that has been discussed at length is that of the self-employed. Both ENOE and ELMPS count them as part of the informal sector.

# **3. Employment Profiles in Egypt**

We start by presenting the respective employment profiles for men and women in Figure 1. The vast majority of men are employed. While male unemployment remained roughly the same between 2012 and 2018, employment fell and the share of men who are out of the labor force (OLF) increased. Instead, in both rounds, around three-quarters of women (20-59 years) are out of the labor force, and only one-fifth of women are employed. Female employment remained constant between the two years, while the share of women being OLF increased and female unemployment declined. Labor market attachment decreased for both genders, with a corresponding decline in employment for men and in unemployment for women.

> 2 Individuals are interviewed for four quarters in a given year and in the first quarter of the following year. 3 Information on the definition of out of the labor force can be accessed through: <u>https://en.www.inegi.org.mx/app/glosario/default.html?p=ENOE15</u> 4 Information on the definition of informality can be accessed through: <u>https://en.www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825060459</u>

5
# **References**

Adam, Christopher, and David Bevan. 2006. “Aid and the supply side: Public investment, export performance, and Dutch Disease in low-income countries.” _World Bank Economic Review_ 20: 261–90.

- Agenor, Pierre-Richard, Nihal Bayraktarb, and Karim El Aynaoui. 2005. “Roads out of poverty? Assessing the links between aid, public investment, growth, and poverty reduction.” Policy Research Working Paper 3490. Washington, DC: World Bank.

- Agenor, Pierre-Richard, Nihal Bayraktarb, and Emmanuel Pinto. 2005. “Achieving the Millennium Development Goals in sub-Saharan Africa: A macroeconomic monitoring framework.” Policy Research Working Paper 3750. Washington, DC: World Bank.

Alfsen, Knut H., Torstein Bye, Solveig Glomsrod, and Henrik Wiig. 1997. “Soil degradation and economic development in Ghana.” _Environment and Development Economics_ 2: 119–43.

Arndt, Channing, Sam Jones, and Finn Tarp. 2015. “What is the aggregated economic rate of return to foreign aid?” _World Bank Economic Review._ 30, 3: 446-474.

- Aune, J. B., and R. Lal. 1995. “The Tropical Soil Productivity Calculator: A model for assessing effects of soil management on productivity” In _Soil Management: Experimental Basis for Sustainability and Environmental Quality III_ , edited by R. Lal and B. A. Stewart. Boca Raton, FL.: CRC Press, 499-520.

Bezemer, Dirk, and Derek Heady. 2008. “Agriculture, development, and urban bias.” _World_ _Development._ 36, 8: 1342-1364.

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confirmed for the Northeast as a whole. These income poverty figures are, however, only part of the story of factors that impact a poor woman’s well being. Furthermore, the data do not reveal anything about domestic violence and other types of discriminations that women often face.

# **3.4. Education**

The Northeast is educationally disadvantaged as compared to the rest of Brazil. Disparities within the region and states as well as amongst poor and non-poor are dramatic. Based on PNAD data from 1981 to 1998 for the total active population (15 to 65 year old), we find that average years of completed education (effective education) of the total active population increased by 1.7 years from 4.5 years in 1981 to 6.2 years in 1998. Average effective education of the active population in Brazil with a per-capita family income below the indigence line increased by a slightly lower rate (by 1.5 years) from 2.2 years in 1981 to 3.7 years in 1998. In 1998, average effective education was highest in Distrito Federal (8.1 years) followed by São Paulo (7.3 years) and lowest in Piaui (4.1 years). In 1998, Rio Grande do Norte and Pernambuco had the highest average effective education in the Northeast. Average effective education of the total active population in Rio Grande do Norte increased by 1.7 years from 3.5 in 1981 to 5.2 years in 1998. Average effective education of the active population in Rio Grande do Norte with a per-capita family income below the indigence line increased the same rate (by 1.7 years) from 2.0 in 1981 to 3.6 years in 1998.

Nevertheless, compared to the situation in the 1970s and 1980s, the disparity between education levels in the Northeast and Brazil have narrowed only a little. The literacy rate of adults increased by 25-percentage points from 46 percent in 1970 to 71 percent in 1996, the literacy rate in the Southeast increased over the same time span from 77 percent to 91 percent (the nationwide figures for 1970 and 1998 are 67 percent and 85 percent respectively). Even though adult literacy occurred at a faster rate in the Northeast, it is striking, that in 1996, the literacy rate in the Northeast had not even reached the level of literacy of the Southeast of 1970. Further, our analysis of PNAD shows that average effective education of the poor in São Paulo (5.1 years) nearly equals the average effective education of the non-poor in Rio Grande do Norte (5.2 years).

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## our model.

The second adjustment concerns the selection of countries from the Amadeus database. Given the known heterogeneity in the degree of coverage of the countries’ firm size distributions, and the importance of minimizing biases in the calculation of average firm sizes, we apply the following selection criteria. Taking Eurostat’s Structural Business Statistics database as the measure of the true size distribution of employment, we keep a country in the sample if 1) the ratio of aggregate manufacturing employment in Amadeus to aggregate manufacturing employment in Eurostat is greater than or equal to 80 percent, and 2) the ratio of manufacturing employment between Amadeus and Eurostat in each bin of Eurostat’s size distribution above the 10-worker threshold (10–20, 20–50, 50–250, 250+) is also greater than or equal to 80 percent. The countries that satisfy these criteria are Belgium, Bulgaria, Finland, France, Hungary, Italy, Latvia, Portugal, Romania, and Spain. Table 2 in Data Appendix B shows the final list of countries together with a brief description of the data sources.

# **4.1 Average Size and Idiosyncratic Distortions**

One advantage of using the firm-level data to compute average size is that it allows us to control for cross-country differences in production structures across broad industries within manufacturing. Thinking of manufacturing as a collection of two-digit sub-industries, average size may then vary across countries due to 1) heterogeneity in average size across firms within two-digit industries under a fixed distribution of production shares across these industries and 2) heterogeneity in production shares for a given distribution of average sizes. Considering this distinction is essential for our analysis given our interpretation of cross-country differences in average firm size as being driven by differences in the degree of allocative distortions and entry barriers. Since, following the literature (Hsieh and Klenow [2009]), misallocation is measured across firms within an industry,<sup>23</sup> our theory is silent about potential misallo-

> 23The reason to restrict the measurement of misallocation to firms within narrow industries is that this is the context in which the underlying assumption of common production

25
|**Table 6: Impact of**|(1)<br>**EU accession on p**|(2)<br>**ost-communis**|(3)<br>**t countries by**|(4)<br>**entry cohort**|
|---|---|---|---|---|
|DiD cohort 1995|2.049***|1.821***|2.127***|1.870***|
||(0.446)|(0.479)|(0.380)|(0.391)|
|DiD cohort 2000|0.788|0.574|0.462|0.373|
||(0.722)|(0.772)|(1.027)|(0.999)|
|Cohort 1995|-0.518|-0.473|-0.539|-0.493|
||(0.840)|(0.813)|(0.918)|(0.888)|
|Cohort 2000|0.275|0.309|0.293|0.156|
||(0.932)|(0.871)|(1.183)|(1.088)|
|Schooling|-0.157|-0.136|-0.278|-0.241|
||(0.248)|(0.237)|(0.286)|(0.277)|
|Polity||0.138*||0.147**|
|||(0.079)||(0.075)|
|Trade|||0.012***<br>(0.005)|0.013***<br>(0.005)|
|1995|0.562|0.595|0.502|0.552|
||(0.403)|(0.394)|(0.416)|(0.409)|
|2000|0.815|0.759|0.739|0.667|
||(0.561)|(0.564)|(0.559)|(0.569)|
|2005|1.836***|1.773***|1.732***|1.637***|
||(0.441)|(0.442)|(0.435)|(0.446)|
|2010|2.328***|2.293***|2.154***|2.065***|
||(0.450)|(0.436)|(0.431)|(0.438)|
|Constant|8.087***|6.790***|8.266***|6.680***|
||(2.173)|(2.177)|(2.247)|(2.316)|
|Prob > chi2|0.000|0.000|0.000|0.000|
|Observations|158|155|155|152|
|Groups<br>Clustered standard errors a<br>*** p<0.01, ** p<0.05, * p<br>"DiD cohort" shows how t<br>Cohort 1995 comprises th<br>EU between 1994 and 199<br>Cohort 2000 comprises th<br>EU between 1998 and 199<br>1995, 2000, 2005, and 201|40<br>t national level in par<br><0.1<br>he treatment effect var<br>e post-communist cou<br>5, namely: Bulgaria, C<br>e post-communist cou<br>9, namely: Estonia, La<br>0 are time controls.|39<br>enthesis.<br>ies by entry coho<br>ntries that signe<br>zech Republic, H<br>ntries that signe<br>tvia, Lithuania, S|40<br>rt.<br>d their associatio<br>ungary, Poland, a<br>d their associatio<br>lovakia, and Slov|39<br>n agreement to the<br>nd Romania.<br>n agreement to the<br>enia.|

18
Figure 4. Historical Investment-to-GDP (%) <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Figure 6. Historical Human Capital Growth (%) Figure 5. Historical TFP Growth (3yma) <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br><!-- End of picture text --> Figure 7. Historical Total Population Growth (%) <!-- Start of picture text --> 2.5 3.5<br>3<br>2<br>2.5<br>1.5<br>2<br>1<br>1.5<br>0.5 1<br>0 0.5<br>0<br>-0.5<br>-1<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Source: World Development Indicators, Penn World Table 10.01 6
Estimating an average cost per day of pre-trial detention would allow us to use the total number of days of pretrial detention to estimate the total loss of income and total loss of tax revenue. The first step is to collect data on pre-trial detention and its consequences (Sarkin, 2009). We might also hypothesize that this loss is of greater effect because of the increased likelihood of expenditure by those disproportionately held in pre-trial detention – those in the ‘bottom 40’. This approach would exclude consideration of inequality-informing variables that pre-trial detention affects: the skills, training and education of the workforce to which detained persons are less exposed (Piketty, 2014). It also excludes the potential relationship to violence of the discontent that is often associated with detention (Thurston and Lebovich, 2013; Dufka, 2016).

The logic of economics, however, does not always inform political decisions about Rule of Law programming, including efforts to drive down average time spent in pre-trial detention. Political actors experience a diversity of pressures, including incentives to harm the economy of a particular territory, for example a region where perceived adversary groups reside (Toft, 2005; Carment et al., 2009). In attempting to persuade governments of the merits of economic interdependence among arguably competing domestic groups, international development actors, employing analytical approaches, should identify and sensitize key commercial stakeholders linked to policy makers.

An important part of the economic rationale for enhancing criminal justice efficacy is to build a nation’s wealth, as well as to stimulate/increase its GDP growth rate. The World Bank has quantitatively identified that equitable advancement in state justice systems also improves economic development. To drive economic development, a 1 percent increase in rule of law institutions’ value will increase intangible capital by 83 percent (Hamilton, 2006). Education and justice system investments, therefore, “...are the most important means of increasing the intangible components of total wealth” (Hamilton, 2006). For low-income countries, therefore, identifying optimally sustainable growth includes identification of policy orientation that maximizes deployment of rents from land and natural resources into physical and human capital (Hartwick, 1977).

As violence tends to affect population subgroups disproportionately, it is important for an economic justification to be informed by how people interact under conditions of socio-economic hardship.<sup>7</sup> Scarce econometric explanations are available of social

> 7 See the following sections on interventions and their political economy.

10
Thus, we use this data to fill for the banking sector when missing, but given that most BIS member reporting countries are advanced, this does not fill many observations.

Figure A2 compares aggregate inflows as measured by our filled data and from the BOP alone, for total external debt of banks and corporates in our samples of AE and EM. We plot annual here for These show that both series tell the same flows clarity. graphs generally story, but there are periods in which accounting for the missing data makes a significant difference. For advanced economy corporates, a significant expansion leading up to the 2008 crisis and a the subsequent contraction are missed. This is due primarily to filling in portfolio debt data for the US and Spain for the 2008 surge, as well as a few other AE for the earlier 2001 peak. For EM, both banks and corporates had much larger flows relative to the BOP measure following the 2008 collapse, driven primarily by filling data for other Figure A3 plots total external debt inflows for government and central bank sectors. Missing U.S. government portfolio debt drives the difference for the AE in panel (a). EM governments and AE central banks are fairly well represented in terms of volume. Note that net inflows can be negative as well as positive, which is the case for EM central banks, where some missing data consists of negative net inflows, which brings our filled data below the raw BOP total. The surge at the end of the sample for EM central banks is driven by China.

To illustrate the quality of our inflow filling series, we compare it with the available BOP data. Figures A4 and A5 illustrates this match by plotting the aggregate inflows for each series by sector, capital flow type, and country group. For each sector and capital flow type, 76
**TABLE 6. Labor productivity and complementarities of ICT**

||F|E|FE‐|2SLS|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|0.029|0.032|0.065|0.052|
||(0.030)|(0.031)|(0.091)|(0.089)|
|ICT * High Productivity|0.083**|0.070*|0.23**|0.23**|
||(0.039)|(0.039)|(0.096)|(0.096)|
|ICT|0.068***|0.063***|0.17***|0.16**|
||(0.021)|(0.021)|(0.065)|(0.065)|
|ICT * Skills|0.024|0.025|0.11**|0.12**|
||(0.032)|(0.032)|(0.053)|(0.052)|
|ICT|0.010|0.018|0.091|0.074|
||(0.025)|(0.024)|(0.091)|(0.090)|
|ICT * Wage|0.10***|0.088***|0.12**|0.13**|
||(0.030)|(0.031)|(0.062)|(0.060)|
|ICT|0.040|0.033|0.10|0.066|
||(0.13)|‐0.13|(0.33)|(0.32)|
|ICT * Internet|0.034|0.037|0.10|0.13|
||(0.13)|(0.13)|(0.32)|(0.32)|
|ICT|0.059**|0.041*|0.17**|0.14*|
||(0.024)|(0.024)|(0.075)|(0.074)|
|ICT * Computers|0.033|0.065**|0.098*|0.15***|
||(0.030)|(0.030)|(0.053)|(0.053)|
|ICT|0.062***|0.062***|0.19***|0.18**|
||(0.019)|(0.020)|(0.069)|(0.069)|
|ICT * Foreign|0.061|0.032|0.044|0.039|
||(0.052)|(0.051)|(0.068)|(0.067)|
|ICT|0.067***|0.070***|0.20***|0.21***|
||(0.020)|(0.020)|(0.073)|(0.073)|
|ICT * Group|0.041|0.00034|0.012|‐0.010|
||(0.042)|(0.043)|(0.060)|(0.060)|
|ICT|0.077***|0.072***|0.23***|0.22***|
||(0.021)|(0.021)|(0.067)|(0.067)|
|ICT * Exp Manager|‐0.023|‐0.024|‐0.049|‐0.044|
||(0.029)|(0.029)|(0.052)|(0.051)|
|Observations|3,391|3,382|3,391|3,382|

Dependent variable: change in log value added per worker. Regressors: ICT investment dummy, and ICT investment dummy interacted with firm‐level indicator variables that are equal to one when: firm labor productivity is above the median, firm share of skilled labor is above the 75th percentile, firm average wage is above the median, firm has internet connection, there are less than 3 employees per computer, firm is of foreign ownership, firm belongs to a group, firm manager has experience in research. Instruments defined as in Table 3. Robust standard errors in parenthesis.

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6 change in the mix of products and the nature of technology.4 Measurement 5 error is also likely, especially in capital stocks and x1& price deflators.

# C. Sector-Specific Structural Change

**For** several reasons, we caution against viewing th.e patterns of adjustment in table 1 as driven exclusively, or even mainly, by trade policy. First, as already noted, the avail4tle price indices may exhibit general biases over time, and some of the variables are measured with error. Second, and more importantly, Chile 4;;derwent many major policy changes between 1967 and 1979, including hy>,. inflation during 1973, a major recession during 1974-76, exchange rate appreciation thereafter, and large increases in ti,e real interest rate. In terms of influence on manufacturing sector aggregates, these forces could easily have masked the effects of commercial policy.

Comparisons of adjustment patterns among the different three-digit industries are more likely to reveal something about the effects of trade liberalization. This is because all industries were subject to roughly the same measurement errors and changes in macro conditions, but different

- 4/ Reductions in the ratio of industrial value added to industrial output are typically observed as the process of economic development unfolds. See, for example, Chenery, Syrquin, and Robinson (1986).

- 5/ We used official price series at the three-digit indust-ial clasificati n level from Chile's Instituto Nacional de Estadastica to deflate outputs, and combined these prices with the 1977 Chilean inputoutput table to impute sector-specific intermediate input price deflators. Experimentation with other price indices did not signficantly change our findings. Further details of our data preparation are available upon request.
<!-- Start of picture text --> 8<br>SN<br>8<br>s<br>4<br>g g<br><!-- End of picture text --> <!-- Start of picture text --> zg<br>Ss<br>=f<br>8 T rr ee ee es<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> <!-- Start of picture text --> oo<br>w o<br>N<br>Ss<br>=<br>2a<br>&<br>xo }<br>&<br>~ 10<br>® 2<br>NT<br>w<br>=<br>©<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text -->
# **Figure 7: Change in Exports by Region**

<!-- Start of picture text --> 25.0<br>20.0<br>Ports<br>15.0<br>Customs<br>Regulations<br>10.0<br>Services<br>5.0<br>0.0<br>OECD East ECA LAC MENA South Sub-<br>Asia Asia Sah.<br>Africa<br>% change<br><!-- End of picture text --> Source: Authors’ calculation.

**Figure 8: Change in Imports by Region**

<!-- Start of picture text --> 10.0<br>9.0<br>8.0<br>7.0<br>Ports<br>6.0<br>Customs<br>5.0<br>Regulations<br>4.0<br>Services<br>3.0<br>2.0<br>1.0<br>0.0<br>OECD East ECA LAC MENA South Sub-<br>Asia Asia Sah.<br>Africa<br>% change<br><!-- End of picture text --> Source: Authors’ calculation.

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