rafmacalaba/datause-encoder-data huggingface dataset:
5

# [Insert Figures 2 and 3 about here]

To get a sense of how much this practice varies across products, whether it is mirrored by the opposite adjustment in times of downward international price spikes such as in the mid-1980s, and whether it has changed much since policy reforms began around the mid1980s, Table 1 reports the average across countries of the percentage point deviation each year of national NRAs for 12 key farm products around their trend value for the sub-periods before and following each of those two spike periods (1972-76 and 1984-88). For the majority of those products that indicator is higher for the period since the late 1980s than in the earlier two non-spike periods. Those annual percentage point deviations since 1988 have been several times the average percentage NRA for the world as a whole (shown in parentheses in the first column of Table 1). During the upward price spike years 1972-76, the NRAs tend to be below the estimates for the periods either side of the spike, whereas in the mid-1980s when prices spiked downwards they tend to be above the NRAs in the more normal periods either side of that spike.

# [Insert Table 1 about here]

That NRAs tend to be above trend in years of low international prices and conversely in years when international prices are high is also clear from Table 2, which shows the extent of the negative correlation between the NRAs for various products and their international price. That coefficient globally and in high-income countries is negative for all but beef, and even in the various developing country regions it is negative in all but one-quarter of the cases. For almost all of those 12 products the (negative) regional correlation is highest for the South Asian region. Among the developing countries it is again rice, sugar and milk that have the highest correlation coefficients.

# [Insert Table 2 about here]

The proportional deviation of the international price of a farm product from its longrun trend value each year turns out to be highly significant when added to equations aiming at explaining NRA differences in the Anderson and Valenzuela (2008) database for those 12 products over the full time series from 1955 to 2007. The other prospective explanatory variables are per capita income, an index of agricultural comparative advantage (farm land per capita) and a dummy for distinguishing export industries. Anderson et al. (2010) show that the price deviation variable is highly significant for the three grains, sugar, cotton and coffee, but is not significantly different from zero for non-staple livestock products and soybean.
- 15 -

**<u>Table 5. Fixed effect and spatial autocorrelation regression results</u>**

||OLS (differ<br><br>|ence)<br>||Spatial regr<br>|ession<br>||
|---|---|---|---|---|---|---|
||Coef.<br>|Std.Err.||Coef.|Std.Err.||
|Δln_L_|0.369|(0.015)|***|0.347|(0.007)|***|
|Δln_R_|0.405|(0.005)|***|0.403|(0.003)|***|
|Δln_I_|0.093|(0.016)|***|0.071|(0.007)|***|
|Δln_F_|0.013|(0.002)|***|0.010|(0.001)|***|
|Δln_TC1_|0.657|(0.167)|***|0.285|(0.238)||
|Δln_TC2_|-0.318|(0.051)|***|-0.143|(0.039)|***|
|constant|0.030|(0.061)||-0.303|(0.056)|***|
|Obs|4624|||4624|||
|F statistic|1220.5||||||
|Wald2||||18480.4|||
|R-squared|0.820||||||
|Spatial parameters:|||||||
|_λ_||||0.078|(0.010)|***|
|_ρ_||||0.305|(0.004)|***|

Note: The dependent variable is Δln _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

One unexpected result is that the elasticity of labor is high. Ethiopian agricultural production is labor-intensive, but there is a common view that the labor force in the agriculture is too abundant to be productive. The marginal productivity is generally considered low. In Ethiopia, 73 percent of the total labor force is absorbed by agricultural activities.<sup>5</sup> Particularly in rural areas, agricultural employment is dominant at 83 percent, compared to 13.5 percent in urban areas. The estimated high elasticity may have captured some impacts of local consumption of agricultural produce, because our labor measurement comes from the global population distribution data, WorldPop, which includes non-agricultural labor force as well. For obvious reasons, holding everything else constant, agricultural production is larger where more people live.

To examine this effect, the labor variable is adjusted using the region-level agricultural employment rates in 2005 and 2013. The 2005 survey data are applied to our 2005 data, and

> 5 According to the recent government statistics: Central Statistical Agency, Ethiopia. (2014). Statistical Report on the 2013 National Labour Force Survey.

(continued)
# **Table A8. Descriptive Statistics for Programs in Brazil**

## **_Using Program-Level Data from the WBSCPS and Administrative Sources_**

|**Variable**|**Progr**<br>**matc**<br>**indivi**<br>**Mean**<br>**(1)**|**ams not**<br>**hed with**<br>**dual data**<br>**Std.Dev.**<br>**(2)**|**Program**<br>**with in**<br>**d**<br>**Mean**<br>**(3)**|**s matched**<br>**dividual**<br>**ata**<br>**Std.Dev.**<br>**(4)**|**P-value**<br>**diff.**<br>**(1) – (3)**<br>**(5)**|
|---|---|---|---|---|---|
|**Panel A. Programquality determinants**||||||
|_Infrastructure_||||||
|Program offers at least one online class|0.34|0.47|0.33|0.47|0.74|
|>30% classes can be taken online|0.13|0.34|0.10|0.30|0.19|
|Number of workshops or labs available for practice|4.57|4.09|5.72|5.61|**0.01**|
|Has enough equipment or tools for practice|0.80|0.40|0.88|0.33|**0.01**|
|Maintenance of largest lab: every year|0.67|0.47|0.70|0.46|0.56|
|_Training and curriculum_||||||
|Curriculum is fixed|0.75|0.43|0.80|0.40|0.23|
|Teaches numerical competencies|0.83|0.38|0.80|0.40|0.47|
|Teaches a foreign language|0.30|0.46|0.34|0.47|0.32|
|Teaches persistence in complex tasks|0.64|0.48|0.70|0.46|0.13|
|Remediation support||||||
|Remediation classes before starting the program|0.46|0.50|0.46|0.50|0.84|
|Remediation classes during the program|0.87|0.34|0.89|0.31|0.47|
|Non-class-based remediation|0.44|0.50|0.45|0.50|0.83|
|Graduation requirements||||||
|Professional association test|0.17|0.37|0.17|0.37|0.99|
|Thesis or research project|0.13|0.34|0.14|0.35|0.81|
|Years since last update to curriculum|1.63|1.66|1.97|2.01|**0.04**|
|Important reasons to update program:||||||
|Government standards|0.48|0.50|0.53|0.50|0.22|
|Employment outcomes or employers' requests|0.85|0.35|0.88|0.32|0.29|
|HEI perception of labor market|0.86|0.35|0.88|0.32|0.42|

72
<!-- Start of picture text --> Based on data for 2019 or the latest year available<br>income || High income || Low income | Lower middle income || Upper middle income<br>High income<br>Upper middle income<br>©<br>€<br>fe)<br>cS)<br>£<br>Lower middle income<br>Low income<br>0 25 50 75 100<br>SPI Score<br>Source: World Bank. Statistical Performance Indicators.<br><!-- End of picture text -->
control for alternative contagion channels, such as trade links, common creditors, and overexposed fund investors. Additionally, we control for domestic and external drivers of crises.

# **4.1.1 Estimating the effect of the initial crisis on uncertainty**

In the first step of our empirical analysis, we estimate the effect of the crisis in Country _A_ on uncertainty in Country _B_ employing fixed effects panel regressions. For this purpose we specify the following test: where _UNCB,t_ signifies the level of uncertainty in a country _B_ at time _t_ , and _CRA,t−_ 1 captures crisis dummies for countries _A_ . This set of initial crisis countries consists of Argentina, Brazil, Mexico, Russia, Thailand, and Turkey, commonly identified as initial crises countries in the literature.<sup>9</sup> We exclude these six countries from the group of countries, _B_ . The other variables in the regression is a set of control variables: _CRB,t−_ 1 represents a dummy of crisis incidences in Country _B_ itself, _ZB,t−_ 1 represents other potential domestic drivers of uncertainty, such as the mean of growth expectations, stock market volatility, and proxies for economic, financial and political risks while _INTt_ represents the interest rate in advanced economies which has shown to have a strong impact on capital flows into and out of emerging markets. _δB_ captures country fixed effects. The level of uncertainty varies significantly across countries, pointing to systematic differences between countries which necessitate the control for country effects. Finally, _εB,t_ captures the error term.

# **4.1.2 Estimating the effect of uncertainty on the probability of a crisis**

In the second step of our empirical analysis, we measure the effect of uncertainty on the probability of a crisis. For this purpose, we run two sets of discrete-dependent variable regressions. First, we specify the following test: where _G_ ( _._ ) represents the standard normal and the logistic cumulative distribution functions, respectively, in the probit and logit estimations we run. The conditioning vector _X_ refers to all the right-hand-side variables. _CTGA,B,t−_ 1 represents the set of control variables for alternative channels of contagion from Country _A_ to Country _B_ . We control for almost

> 9See sources cited in the Introduction.

15
intervention which attempted to improve the quality of participation and deepen democracy within the panchayat system.

Bureaucrats and activists from the Karnataka State Institute Development (KSIRD) designed the intervention. They were keen to develop a People’s Campaign that was contextualized to the low literacy, high inequality, and semi-arid context of north Karnataka. The KSIRD, at the time, was responsible for all panchayat training in the state and had many years of experience in the field. The goal of the intervention was to make village government more accountable to citizens by (i) increasing participation in gram sabhas, (ii) making citizen engagement with GPs more informed, and (iii) improving cooperation between GPs and low-level government officials.

The “treatment” consisted of three phased interventions mimicking many aspects of the Kerala People’s Campaign: The first phase in the intervention, a citizenship engagement program which lasted one week, began with a pair of facilitators visiting every neighborhood in the village encouraging citizens to attend neighborhood meetings or “ward sabhas.” In the ward sabhas citizens were informed about a schedule of training that instructed citizens in processes of participatory planning and deliberative decision-making, and disseminated information about the budgets, programs and procedures of the gram panchayat. Among other things, the initial intervention explained the purpose and value of the gram sabha, rights of citizens such as the right to information, and information on how to examine panchayat records such as budgets, and minutes of meetings. At the end of the week, the project facilitators, who were called Resource Persons (RPs) held a special gram sabha where priorities were finalized and listed in a Village Action Plan that was ratified in the gram sabha.

The second phase, which occurred immediately after the ratification of the Village Action Plan, consisted primarily of a meeting with local bureaucrats to reach an implementation agreement where the bureaucrats committed to provide funding and technical support for projects selected by the Village Action Plan over the course of the year.

In the third phase, which was spread over a period of two years, the GP was monitored for approximately two days every month by KSIRD’s team of RPs with “handholding” to ensure the program’s progress. In these follow up visits, citizen initiatives were tracked and assisted by the RPs. Importantly the RPs ensured that gram sabhas were held according to schedule. They also tried to follow up on the implementation of the Village Action Plan and helped citizens and GP officials if they needed to visit local bureaucrats.

6
this increased sophistication of the export basket has not been strong enough to propel GDP growth to rates that exceed those of other middle‐income countries.

Figure 5: EXPY for Morocco and Selected Textile Exporters (1990 – 2010) <!-- Start of picture text --> Hong Kong SAR, China<br>ln(GDPpc) = -12.38 + [ 2.26 x ln(EXPY) ] Italy<br>Portugal<br>R2 = 0.6563 Obs. 186 Lithuania<br>BulgariaPanamaTurkey<br>Tunisia<br>China<br>Sri Lanka<br>Indonesia<br>Vietnam India<br>Pakistan<br>Bangladesh<br>Nepal<br>8 8.5 9 9.5 10<br>Income Potential of the Export Basket [ Log of EXPY ]<br>Morocco 1990 - 2010 Selected Textile Exporters<br>11<br>10<br>9<br>8<br>Log of GDP per capita<br>7<br>6<br><!-- End of picture text --> Note: Points for Selected Textile Exporters corresponds to year 2010 Source: Authors’ calculations using Comtrade Data Figure 6, a map of the PS in 2010 that reflects proximities between products, is relevant to a discussion of issues related to the income potential of country’s exports and how it may change over time.<sup>7</sup> The map highlights the positions of Morocco’s classical, emerging and marginal products, reflecting the data on long‐term shifts in _RCA_ for Morocco between 1980 and 2010, discussed in Section 4.1 It is evident that most classical exports (in red) and emerging exports (in blue) are located in the periphery of the PS, meaning that their underlying capabilities are not easily transferable to other, perhaps, more sophisticated products; given this, these products offer only a limited diversification potential. By contrast, marginal products (in green) are closer to the core of the PS, which is the densest part. This may mean that Morocco is building the capacities to export these products but that these capacities are not yet mature enough to permit exports in much larger quantities. A closer inspection is needed to evaluate the feasibility of marginal merchandise exports, among other things considering the extent to which they are affected by trade policies, favoring or disfavoring these sectors.

> 7 It is important to emphasize that the PS network shown in Figure 6 represents the global map of the product space; it was constructed using global export data disaggregated by product and country, on the basis of which we computed conditional probabilities of co‐exporting pairs of products with RCA>1 for both. Hence, the shape of the PS network is not country specific; however, in this global map, we highlight Morocco’s classical, emerging, and marginal products.

11
# **Is 3D Printing a Threat to Global Trade? The Trade Effects You Didn’t Hear About**<sup>**†**</sup>

**Caroline Freund, Alen Mulabdic, Michele Ruta**<sup>∗</sup>

World Bank _Keywords_ : 3D Printing, Trade, Comparative Advantage, Developing Countries _JEL Codes_ : F14, O33 † We are grateful to Simeon Djankov, Nuno Limão, Aaditya Mattoo, Denis Medvedev, Erik Van der Marel, and seminar participants at the World Bank and the workshop on “Europe 4.0: Sharing the New Economy” for helpful comments and suggestions. Errors are our responsibility only.

>  World Bank, 1818 H Street, Washington DC, USA. Caroline Freund, Email: cfreund@worldbank.org; Alen Mulabdic, Email: <u>amulabdic@worldbank.org; Michele Ruta, Email: mruta@worldbank.org.</u> 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.
Taking the international poverty line to be the rural line rather than the urban line is a somewhat arbitrary choice, although is at least broadly consistent with the original idea of the “$1 a day” poverty line as a deliberately conservative line; indeed, the original set of poverty lines on which the World Bank’s international line was based were for rural areas (Ravallion et al., 1991; World Bank, 1990). The precise line used by the Bank is $32.74 per month ($1.08=$32.74x12/365 per day), which is the median of the lowest 10 poverty lines in the original compilation of (largely rural) poverty lines, as documented in Ravallion et al., (1991) (although the PPPs have been updated and revised since then; see Chen and Ravallion, 2004, for details).<sup>14</sup> By implication, our aggregate poverty count will tend to increase, given that urban poverty lines are generally above those for rural areas.

Appendix 1 provides a more formal exposition of our approach, and how it differs from past methods of measuring the extent of the urbanization of poverty.

Table 1 gives a regional summary of the poverty lines while Appendix 2 gives the urbanrural poverty line differential by country. On average, the urban poverty line is about 30% higher than the rural line. However, the numbers vary from region to region. In Eastern European and Central Asia, the urban poverty line is only 5% higher on average while in Latin America and the Caribbean it is 44% higher on average.

As can be seen in Figure 1, there is a tendency for poorer countries to have higher ratios of the urban line to the rural line; the correlation coefficient of the poverty-line ratio with the rural headcount index for $1 a day is 0.518 in 1993 (n=89); for the $2 a day headcount index the correlation is 0.521 (both are significant at better than the 1% level). This is consistent with the hypothesis that internal market integration tends to improve as countries become less poor.

In all cases, the distributional data were in nominal terms, to which we applied the appropriate urban or rural poverty lines. In two-thirds of cases, the PA gives explicit urban and rural poverty lines, and we used these to construct the COL ratio and (hence) the urban poverty

> 14 Chen and Ravallion (2001) also estimate the expected poverty line in the poorest country, which is $1.05 per day, although there is of course a variance around this estimate; the 95% confidence interval is ($0.88, $1.24).

11
<u>Table 4: The impact of stricter employment protection measures on the probability of having short, medium, and long-term job tenure, 1995-2020.</u>

|<br>|<br>Less<br>|<br>than 1<br>|Length of t<br>5 t<br>|enure (years)<br>o 10<br>|More<br>|than 10<br>|
|---|---|---|---|---|---|---|
|Sub-samples|Coeff.|_Std. Error_|Coeff.|_Std. Error_|Coeff.|_Std. Error_|
|<br>|<sup>**</sup>|**_Mor_**<br>|**_e difficult to dis_**<br>|**_miss regular w_**<br>|**_orkers_**<br>||
|Total|-0.861|_0.333_|0.021|_0.317_|0.347|_0.931_|
|_Gender_|||||||
|Males|-0.788<sup>*</sup>|_0.346_|0.308|_0.330_|-0.863|_0.939_|
|Females|-0.940<sup>**</sup>|_0.333_|-0.285|_0.346_|1.774<sup>**</sup>|_0.989_|
|_Education_|||||||
|Lower secondary|-0.987<sup>**</sup>|_0.480_|-0.323|_0.375_|0.400|_1.078_|
|Upper secondary|-1.024<sup>**</sup>|_0.364_|0.132|_0.339_|0.207|_0.973_|
|High: Third level|-0.728<sup>**</sup>|_0.284_|0.012|_0.306_|-0.405|_0.849_|
|_Age_|||||||
|30-40|-0.764<sup>**</sup>|_0.349_|0.484|_0.459_|1.330|_1.010_|
|40-50|-0.395|_0.245_|-0.316|_0.437_|0.334|_1.004_|
|50+|-0.382<sup>**</sup>|_0.188_|0.183|_0.394_|-0.601|_0.836_|
|||<br>**_Mor_**|<br>**_e difficult to hi_**|<br>**_re temporary wo_**|<br>**_rkers_**<br>|<br>|
|Total|-0.121|_0.157_|-0.230|_0.165_|1.233<sup>**</sup>|_0.408_<sup>**</sup>|
|_Gender_|||||||
|Males|-0.157|_0.161_|-0.169|_0.164_|1.197<sup>**</sup>|_0.407_<sup>**</sup>|
|Females|-0.072|_0.157_|-0.249|_0.185_|1.260<sup>**</sup>|_0.441_<sup>**</sup>|
|_Education_|||||||
|Lower secondary|-0.159|_0.211_|-0.111|_0.195_|0.915<sup>*</sup><br>|_0.461_<sup>*</sup><br>|
|Upper secondary|-0.167|_0.165_|-0.085|_0.179_|1.023<sup>*</sup>|_0.425_<sup>*</sup>|
|High: Third level|0.010|_0.137_|-0.605<sup>***</sup>|_0.163_|1.603<sup>***</sup>|_0.364_<sup>***</sup>|
|_Age_|||||||
|30-40|-0.189|_0.164_|0.569<sup>**</sup>|_0.245_|0.148|_0.475_|
|40-50|-0.189<sup>*</sup>|_0.112_|-0.649<sup>***</sup>|_0.202_|2.020<sup>***</sup>|_0.424_|
|50+|-0.105|_0.084_|-0.677<sup>***</sup>|_0.170_|1.833<sup>***</sup>|_0.345_|

_Note_ : Coefficients and standard errors are estimated by the panel regression of 26 marginal effects of the year dummies estimated on subsamples of 29 countries on the linear time trend and the EPR and EPT indexes. The marginal effects are derived from estimation of the probability of a worker having one of three durations of job tenure. Estimation of the probability of a worker having less than one year of job tenure is conducted on a sample of respondents 20 and older. The probability of a worker having 5–10 years of tenure is estimated on a sample of respondents 25 and older. The probability of having tenure of more than 10 is estimated on a sample of workers 30 and older. Bootstrapped standard errors.<sup>***</sup> indicates that the coefficient is significant at 1% level,<sup>**</sup> - at 5% level,<sup>*</sup> - at 10% level.

40
**Figure 2.** Effect of policy on grade completion rates

<!-- Start of picture text --> A) By Gender B) By Gender C) By Urban/Rural D) By Urban/Rural<br>in Urban Areas in Rural Areas for Men for Women<br>A1) Men B1) Men C1) Urban D1) Urban<br>.3 .3 .3 .3<br>.2 .2 .2 .2<br>.1 .1 .1 .1<br>0 0 0 0<br>6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11<br>A2) Women B2) Women C2) Rural D2) Rural<br>.4 .4 .4 .4<br>.3 .3 .3 .3<br>.2 .2 .2 .2<br>.1 .1 .1 .1<br>0 0<br>0 0<br>6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11<br>A3) Men-Women B3) Men-Women C3) Urban-Rural D3) Urban-Rural<br>.3 .3 .3 .3<br>.1 .1 .1 .1<br>-.1 -.1 -.1 -.1<br>-.3 -.3 -.3 -.3<br>6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11 6 7 8 9 10 11<br>Grade Level<br>Change in Fraction Completed and 95% CI<br><!-- End of picture text --> _Source_ : Authors’ calculations based on 2003 and 2008 Turkish Demographic and Health Survey.

44
**Table 5: Base model—By model design—Fixed effects—regression on firm profitability as dependent variable**

||No Dummies|Year Dum.|+Reg. Dum.|Full Model|
|---|---|---|---|---|
|**Treatment (ITT)**|-0.00395|-0.00338|-0.00327|-0.00307|
||(-0.36)|(-0.31)|(-0.30)|(-0.28)|
|Reform Period|-0.00852**|-0.0322***|-0.0323***|-0.0318***|
||(-2.00)|(-3.44)|(-3.45)|(-3.40)|
|Constant|0.0462***|0.0534***|0.0639|-0.0183|
||(20.68)|(14.61)|(0.87)|(-0.23)|
|Year Dum.|No|Yes|Yes|Yes|
|Region Dum.|No|No|Yes|Yes|
|Sector Dum.|No|No|No|Yes|
|Observations|1 363 022|1 363 022|1 363 022|1 363 022|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

**Table 6: Base model—By size—Random effects—regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|-0.000363|0.00309|-0.445|-0.0417|
||(-0.03)|(0.32)|(-0.90)|(-0.51)|
|Pre-reform<br>youth share|0.0439***|0.0571***|0.439|-0.0586|
||(5.23)|(7.25)|(1.04)|(-0.89)|
|Reform Period|-0.0318***|-0.0321***|0.0407|-0.000234|
||(-3.37)|(-3.31)|(0.78)|(-0.01)|
|Pre-reform<br>Education level|-0.0570***|-0.0539***|-0.670*|0.0497|
||(-6.15)|(-5.99)|(-1.77)|(1.58)|
|Pre-reform firm<br>size|-0.0000192**|-0.00398***|0.000211|0.00000337|
||(-2.20)|(-7.57)|(0.58)|(1.03)|
|Constant|0.198***|0.207***|1.051|-0.0809|
||(9.90)|(10.42)|(1.63)|(-0.60)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 363 022|1 321 871|34 695|7 160|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

21