rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10. RIF decomposition of the gender differential in agricultural productivity with region fixed effects (2016)**

||**Y=**|**RIF estimate**|**of Log [ Self-**|**reported Pro**|**ductivity (XO**|**F/ha)]**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|**overall**||||||||||
|Male headed households|10.260***|10.872***|11.252***|11.532***|11.805***|12.020***|12.258***|12.544***|12.995***|
||(0.063)|(0.049)|(0.041)|(0.037)|(0.032)|(0.031)|(0.033)|(0.039)|(0.042)|
|Female Headed households|9.850***|10.533***|10.947***|11.275***|11.566***|11.843***|12.138***|12.508***|12.971***|
||(0.175)|(0.101)|(0.094)|(0.089)|(0.081)|(0.075)|(0.078)|(0.095)|(0.111)|
|difference|0.411**|0.339***|0.305***|0.257***|0.239***|0.177**|0.119|0.037|0.024|
||(0.181)|(0.109)|(0.099)|(0.094)|(0.083)|(0.079)|(0.081)|(0.098)|(0.118)|
|**% gap**|**34%**|**29%**|**26%**|**23%**|**21%**|**16%**|**11%**|**4%**|**2%**|
|<br>explained|0.201|0.145|0.123|0.188**|0.096|0.083|0.018|0.034|-0.051|
||(0.182)|(0.126)|(0.104)|(0.095)|(0.086)|(0.078)|(0.085)|(0.090)|(0.115)|
|unexplained|0.210|0.194|0.182|0.069|0.143|0.095|0.102|0.003|0.075|
||(0.249)|(0.164)|(0.131)|(0.121)|(0.109)|(0.100)|(0.096)|(0.106)|(0.145)|
|**explained**||||||||||
||-0.375***|-0.310***|-0.349***|-0.340***|-0.319***|-0.305***|-0.312***|-0.361***|-0.404***|
|Log [Total land area cultivated in ha]|(0.075)|(0.054)|(0.046)|(0.040)|(0.037)|(0.037)|(0.039)|(0.046)|(0.066)|
|<br>Household head age (years)|0.006|-0.039*|-0.014|-0.025*|-0.013|-0.008|-0.020|-0.043**|-0.028|
||(0.036)|(0.021)|(0.017)|(0.015)|(0.013)|(0.013)|(0.014)|(0.018)|(0.024)|
|Married|0.268**|0.139|0.083|0.066|0.007|0.042|-0.000|-0.016|0.112*|
||(0.136)|(0.092)|(0.072)|(0.063)|(0.055)|(0.051)|(0.052)|(0.061)|(0.061)|
|Divorced/Separated|-0.021|-0.010|-0.001|-0.011|-0.003|-0.010|-0.003|0.002|-0.015|
||(0.029)|(0.018)|(0.015)|(0.014)|(0.011)|(0.011)|(0.011)|(0.012)|(0.016)|
|Widowed|-0.136|-0.130|-0.060|0.035|-0.002|-0.013|0.015|0.086|0.032|
||(0.190)|(0.109)|(0.086)|(0.073)|(0.063)|(0.060)|(0.063)|(0.072)|(0.083)|
|Household head has Primary|-0.010|-0.006|-0.006|-0.006|0.001|-0.000|0.001|0.002|-0.002|
|<br>education|(0.010)|(0.006)|(0.005)|(0.005)|(0.004)|(0.004)|(0.004)|(0.005)|(0.006)|
|Household head has Secondary|-0.045**|-0.020|-0.013|-0.011|-0.008|-0.004|0.001|0.011|0.003|
|<br>education|(0.020)|(0.014)|(0.011)|(0.009)|(0.008)|(0.008)|(0.009)|(0.011)|(0.014)|
|Household head has Tertiary|-0.015*|-0.010*|-0.007**|-0.006**|-0.004*|-0.003|-0.002|-0.003|-0.003|
|<br>education|(0.009)|(0.005)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|
|Household head disability|-0.018|-0.005|-0.011|-0.003|0.002|0.000|0.004|0.006|0.002|
||(0.015)|(0.010)|(0.009)|(0.008)|(0.006)|(0.006)|(0.007)|(0.008)|(0.009)|
|Dependency ratio|-0.018|-0.016|-0.014|-0.005|-0.004|-0.003|-0.003|0.008|0.015|

24
# **Table A17: Identifying the $1.90 poor based on covariates of poverty**

_Yemen 2014_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|-0.001***|-0.007***|0|-0.011***|-0.017***|0|
|Asset index|-0.007***|-0.032***|0|-0.071***|-0.096***|0|
|Literacy|-0.012*|-0.070***|0|-0.129***|-0.187***|0|
|Access to electricity|-0.016**|-0.089***|0|-0.205***|-0.278***|0|
|Piped drinking water|-0.010**|-0.037***|0.0331|-0.091***|-0.118***|0.0331|
|Improved sanitation|-0.009|-0.067***|0|-0.146***|-0.203***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A18: Identifying the $3.20 poor based on covariates of poverty**

_Yemen 2014_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|0.000|-0.002**|0.0417|-0.019***|-0.021***|0.0417|
|Asset index|-0.001|-0.013***|0.0001|-0.133***|-0.146***|0.0001|
|Literacy|0.000|-0.003|0.7626|-0.200***|-0.204***|0.7626|
|Access to electricity|-0.005|0.006|0.394|-0.334***|-0.323***|0.394|
|Piped drinking water|0.000|0.006|0.661|-0.178***|-0.173***|0.661|
|Improved sanitation|-0.005|-0.017|0.2762|-0.282***|-0.294***|0.2762|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 56
# **Table A22:  Merchant International Group Gray Area Dynamics (MIG)**

**Data Provider** Merchant International Group **Description** Commercial risk rating agency  headquartered in London, United Kingdom **Website** www.merchantinternational.com **Data Source** Gray Area Dynamics. **Type** Expert assessment **Respondents** Merchant International Group staff **Frequency** Quarterly since 1994 **Coverage** Global sample of countries **Public Access** Commercially-available **Description** Provides assessments of risks to foreign investor posed by 10 risk factors assessed on a 10point scale.

||2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Political Stability and Lack of Violence**|||||||||
|Extremism|X|X|X|X|X|..|..|..|
|**Government Effectiveness**|||||||||
|Bureaucracy|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||
|Unfair Trade|X|X|X|X|X|..|..|..|
|Unfair Competition|X|X|X|X|X|..|..|..|
|**Rule of Law**|||||||||
|Legal Safeguards|X|X|X|X|X|..|..|..|
|Organized Crime|X|X|X|X|X|..|..|..|
|**Control of Corruption**|||||||||
|Corruption|X|X|X|X|X|..|..|..|
|_Country coverage_|**156**|**155**|**155**|**155**|**118**|**..**|**..**|**..**|
|_Year of Publication_|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

60
dence rejects the hypothesis that the set of estimated Rybczynski coefficients are statistically equivalent across technological country groups. Furthermore, these international differences are at least partly due to differences in IPRs, after controlling for factor endowments, relative factor prices and Hicks-neutral productivity differences across countries.

The rest of this paper is organized as follows. Section 2 discusses the related literatures. Section 3 introduces the model. Section 4 solves the equilibrium of the theoretical model. Section 5 presents the empirical strategy. Section 6 discusses the empirical results, including alternative explanations of heterogeneous Rybczynski coefficients. Section 7 concludes.

# **2 Related Literature**

At least two distinct literatures are related to our model and empirical application. The first one is the trade literature on factor proportions and trade patterns. The second one concerns biased technical change.

## **2.1 Trade and factor proportions**

This literature can be divided into two strands of research. One explores the implications of the factor proportions theory under the assumption that all countries have access to the same technologies. A second assumes that there are Hicks-neutral technology differences across countries.

In the first strand, Harrigan[16] examines the production side of the factor proportions model. The author employs manufacturing outputs and factor endowments data for up to 20 OECD countries during 1970-1985. The most robust evidence suggests that capital abundance is a source of comparative advantage in most of the sectors, but the effects of skilled- and unskilledlabor are not clear. The signs of the Rybczynski coefficients, however, change across econometric specifications.

In the same vein, but motivated by a slightly different question, Schott[33] investigates whether developed and developing countries specialize in different subsets of products as a result of their differences in factor endowments. He proposes a methodology that distinguishes single- from multiple-cone 2
# **References**

- Aguiar, M. and G. Gopinath (2007). “Emerging Market Business Cycles: The Cycle Is the Trend.” _Journal of Political Economy_ 115(1): 69-102.

- Aksoy M.A. and J. Beghin (2004). _Global Agricultural Trade and Developing Co_ untries. Washington, DC: World Bank.

- Aksoy, M.A and B.M. Hoekman (2010). _Food Prices and Rural Poverty_ . Washington, DC: World Bank.

- Aldasoro, I., S. Avdjiev, C. Borio, and P. Disyatat (2020). “Global and Domestic Financial Cycles: Variations on a Theme.” BIS Working Paper 864. Basel, Switzerland: Bank of International Settlements.

- Baffes, J. (2013). “A Framework for Analyzing the Interplay among Food, Fuels, and Biofuels.” _Global Food Security_ , 2(2): 110-116.

- Baffes, J. (2011). “Cotton Subsidies, the WTO, and the ‘Cotton Problem’.” _The World Economy_ , 34(9): 1534-1665.

- Baffes, J. (2007). “Oil Spills on Other Commodities.” _Resources Policy_ , 32(3): 126-134.

- Baffes, J., A. Kabundi, and P. Nagle (2021). “The Role of Income and Substitution in Commodity Demand.” _Oxford Economic Papers_ , forthcoming.

- Baffes, J. and X. Etienne (2016). “Analyzing Food Price Trends in the Context of Engel's Law and the Prebisch-Singer Hypothesis.” _Oxford Economics Papers_ , 68: 688-713.

- Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker (2015). “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.” Policy Research Note 1. Washington, DC: World Bank.

- Baffes, J. and H. De Gorter (2005). “Experience with Decoupling Agricultural Support.” In _Global Agricultural Trade and Developing Countries,_ ed. M.A. Aksoy and J. Beghin. Washington, DC: World Bank.

- Basu, R.B. and R.K. Gallardo (2021). “Economic Issues Related to Long-Term Investments in Tree Fruits.” _Choices_ , 36(2): 1-7.

- Baumeister, C. and J.D. Hamilton (2019). “Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks.” _American Economic Review_ , 109(5): 1873-1910.

- Baumeister, C., D. Korobilis, and T.K. Lee. (2021). “Energy Markets and Global Economic Conditions.” _Review of Economics and Statistics_ , in press.

- Baxter, M. and R.G. King (1999). “Measuring Business Cycles: Approximate Band-pass Filters.” _The Review of Economics and Statistics_ , 81(4): 575-593.

- Bernanke, B. (1983). “Irreversibility, Uncertainty, and Cyclical Investment.” _Quarterly Journal of Economics_ , 98(1): 85-106.

_— 27 —_
Table 5 shows an encouraging rate of correctly classified cases (accuracy rates ranging from 70.57 to 81.64 percent). However, it is important to note that the sensitivity measure, which is relevant in cases where the dependent variable is less likely to take the value of 1 compared to 0, shows that overall, the logistic models, have a sensitivity level ranging from 15.46 to 26.33 percent. These levels, although quite low, are similar to Sansone (2019) who uses LASSO to identify predictors that are then used in logistic regressions to measure school dropout. In their study, the sensitivity levels of the logistic models range from 23 to 28 percent. For women, the performance of the logistic models using LASSO Stability Selection predictors is better than one using Random Forest predictors, with higher levels of accuracy and sensitivity. For men, however, the reverse is true. Considering separate women and men subsamples is therefore a good option to choose the best model for prediction purposes. The low levels of sensitivity also show that aspirations to migrate may be difficult to predict, even with a large set of explanatory variables.<sup>20</sup>

**Table 6. Performance indicators of the Random Forest models**

|**Model**|**Out-of-bag error**|**Accuracy**|**Sensitivity**|
|---|---|---|---|
|Female Random Forest<br>model using all variables|17.6%|19.21%|0.32%|
|Male Random Forest<br>model using all variables|17.55%|31.74%|6.25%|
|Female Random Forest<br>model using top 30<br>predictors|30.04%|19.15%|0.96%|
|Male Random Forest<br>model using top 30<br>predictors|29.99%|32.5%|6.31%|

Source: Authors’ calculations from DESMIGRATIF (2018).

In addition, we check the robustness of our results using Random Forests’ internal error measurement. This built-in feature is not available for the LASSO Stability Selection algorithm. Table 6 shows the performance of the Random Forest models using accuracy and sensitivity indicators as well as the outof-bag error. The accuracy and sensitivity are calculated in the testing sample whereas the out-of-bag error is calculated during the bootstrap aggregation process of the Random Forest, in the training sample. The table compares the performance of Random Forest using the complete list of 301 variables as well as restricted models with only the top 30 predictors. The table shows that the

> 20 Note that we also tested the accuracy and sensitivity levels of logistic estimations with top predictors obtained from logistic LASSO instead of linear LASSO. These results (available upon request) show no improvement in prediction accuracy.

30
# FIGURE 2. CUMULATIVE MORTALITY RATES BY MEASLES VACCINATION STATUS: MATLAB INTERVENTION AREA, 1982-85

<!-- Start of picture text --> 70-<br>Unvoccinated<br>0 60 - -<br>0<br>0<br>o 00<br>50-<br>a. /<br>d \/accinctedVy<br>- '<br>40-<br>X 230-<br>E 2 0 - O<br>E<br>0-<br>0 3 6 9 12 15 18 21 24 27 30 33 36 39 42<br><!-- End of picture text --> Months after vaccination
Tablc 3.1

# **NEPA and Tianjin EPB Data on Pollution Abatement** and Costs for 260 Factories (Variable Labels from Equation **(4.3)** in Parentheses)

# Abatement Cost

Water Pollution Abatement Expenditure (C) Equipment Operations and Maintenance

# **Waste Stream Volume, Influent and EMuent**

Annual Volume of Waste Water (1.) (W) Influent (I) and Effluent (E) Concentrations in Waste Water (mg/l): Biological Oxygen Demand (BOD) Chemical Oxygen Demand (COD) Suspended Solids (SS) Other Pollutants 12
# VII. **OUT-OF-POCKET PAYMENTS**

VHI need not necessarily cause out-of-pocket payments to fall. If the demand for health care covered by VHI is especially elastic with respect to price, or if the demand for health care generally is especially sensitive to the reduction of risk and uncertainty, VHI could conceivably result in _higher_ out-of-pocket payments.

Both surveys contain estimates _for the previous twelve months_ of cash and in-kind payments for health care. These data are collected from _all_ respondents, whether or not they had been ill in the previous 4 weeks. In addition, the 1997/98 survey allows a reliable estimate to be made of out-of-pocket spending in the 4 weeks prior to the interview<sup>17</sup> , and permits annual health spending to be broken down into inpatient and other health spending. The numbers are in thousands of Vietnamese dong. The double-difference estimate of annual out-of-pocket spending is negative and significant (Table 9), in contrast to the single-difference estimate for 4- week and annual total out-of-pocket spending derived from the 1997/98 data alone. The last two lines of Table 9 point to VHI increasing—albeit not significantly—inpatient care spending, and having a dampening effect on spending on other types of care.

_Table 9: Estimates of VHI impact on out-of-pocket health spending_

||Treatment<br>effect|z-stat|
|---|---|---|
|**Double difference on longitudinal data**|||
|Change in out-of-pocket spending between 1992/93 and 1997/98|-51.967|-2.52|
|**Single difference on 1997/98 data**|||
|Out-of-pocket spending in last 4 weeks|-2.530|-0.27|
|Out-of-pocket spending in 1997/98|-18.744|-0.96|
|Inpatient expenditure in 1997/98|8.636|1.08|
|<br>Non-inpatient out-of-pocket spending 1997/98|-27.380|-1.67|

Jowett et al. (2003) also found that VHI reduced out-of-pocket payments, though there are some differences between their study and ours are worth noting: their survey was not nationally representative; VHI coverage was restricted to voluntary coverage (their data were collected in the city of Hai Phong that succeeded for a while in enrolling adults as well as school children in the voluntary scheme); out-of-pocket payment data were collected only from those reporting illness; and their results refer only to those reporting positive payments. Trivedi (2003) found some evidence from the 1997/98 data of VHI _increasing_ out-of-pocket expenditures<sup>18</sup> , but the effect was not significant when commune fixed effects were included in the regression model.

> 17  The 1992/93 survey also asked about spending in the previous 4 weeks, but only of those reporting illness and only in respect of first visits to each provider type.

> 18  Spending was defined in Trivedi’s analysis as spending over the past 4 weeks _plus_ spending over the past 12 months.

17
distance to the next settlement of more than 2,000 inhabitants is positively associated with job creation, changes in their proximity to large farms have no significant impact on their propensity to take up permanent or temporary work. This supports the notion that large mechanized farms may be good at increasing the intensity of land use but not at generating local employment. Fourth, for all crops except sorghum, which is often grown because of drought resistance, large farm presence significantly enhanced small farmers’ resilience and ability to withstand climatic shocks. As they were almost exclusively confined to large farms growing the same crop, learning about specific measures to conserve moisture seems a plausible channel underpinning such effects.

Lack of information on non-operational farms implies that our results are valid only for farms operational in 2014. To the extent that this or our inability to check for outmigration does not bias results, this implies that, in Ethiopia, large farm establishment generated modest but positive spillovers. It also suggests that policies affecting investors’ location and crop choices, in addition to those governing levels, use, and destination of lease fees, will affect social and economic impacts from the establishment of large farms. Our finding evidence of positive spillovers even in a setting where neither responses to investor demand nor links to technology transfer or integration with value chains had been well coordinated, suggests that a more integrated approach may well result in higher spillover effects. Experimentation and rigorous monitoring, relying on a wider range of data sources that would include administrative records and remotely sensed imagery, could help to identify ways of enhancing spillovers and making some of the associated data public to facilitate third-party monitoring. Such monitoring could increase investment flows by a growing set of financial players who want to demonstrate compliance with environmental, governance, and social standards.

While we obtain interesting insights, this is only a first step with ample scope for expansion in a number of possible directions. First, it would be worthwhile to explore alternative outcome variables, especially prices. Second, there seems ample potential to deepen understanding of the channels through which spillover effects may materialize. Ways to do so would be to link survey to administrative data, or to exploit impacts of exogenous variation in large farms’ contractual terms or their interaction with local communities. Third, to interpret and contextualize the magnitudes of effects found here, it will be useful to compare their size to those from other types of public goods such as irrigation or technology transfer as well as private investment, including establishment of up-stream agro-processing facilities to integrate small producers into value chains. This is a challenging agenda of great importance for rural development and poverty reduction. The fact that, after the 2008 boom, many countries are left with a large number of non-viable large farms could provide a unique opportunity in this respect.

16
# **Occupational Hazards:**

# **Migrants and the Economic and Health Risks of COVID-19 in Western Europe**

Laurent Bossavie Daniel Garrote Sanchez Mattia Makovec Caglar Ozden

**World Bank**

Originally published in the <u>Policy Research Working Paper Series on</u> _December 2021._ This version is updated on _April 2022._ To obtain the originally published version, please email prwp@worldbank.org.

JELcodes: J17, J21, J28, J61 Keywords; migrant workers, COVID-19 pandemic, labor market vulnerability.
Figure 7: ICT Abundance and Industrial Composition <!-- Start of picture text --> (A.1) Model Fit: GGDC10 (A.2) Model Fit: WDI<br>45-Degree Line 45-Degree Line<br>2000 until 2000<br>2010 after 2000<br>.02 .04 .06 .08 .1 .02 .04 .06 .08 .1<br>Industry Predicted ICT Value/NICT Value Industry Predicted ICT Value/NICT Value<br>(B.1) Ind. Pred. and Real Income: GGDC10 (B.2) Ind. Pred. and Real Income: WDI<br>2000 until 2000<br>2010 after 2000<br>-8 -7 -6 -5 -4 -3 -8 -6 -4 -2<br>Log Real GDP Per Capita Log Real GDP Per Capita<br>.15 .25<br>.2<br>.1<br>.15<br>.1<br>ICT Value /NICT Value .05 ICT Value /NICT Value<br>.05<br>0 0<br>.1<br>.1<br>.08<br>.08<br>.06 .06<br>Industry Predicted ICT/NICT .04 Industry Predicted ICT/NICT .04<br>.02 .02<br><!-- End of picture text --> _Notes:_ Panel A displays the observed ratios against the ones predicted by equation (12). Panel B plots the industry predictions against log real GDP per capita. Column 1 is based on GGDC10 data while column 2 is based on WDI data, respectively.

observed in the US over the period 1993-2011, again based on Eden and Gaggl (2014). Finally we assume _r_ = 0 _._ 03, which resembles the average real return consistent with equation (9). As described above, we consider two alternative measures for _Yh,j_ , one based on the GGDC10 database and one based on the WDI. Again, it is worth emphasizing that, by construction, the only source of cross-country variation in these predicted values is due to heterogeneity in industrial composition measured by value added.

Panel A of Figure 7 illustrates the “fit” of these predictions based on industrial composition.

marginal product. Specifically, this sometimes happens in very NICT intensive industries in which we likely overestimate the marginal product of NICT based on our aggregate approximation.

23
7 in the 1970s, but it accelerated to over 3 percent in the 1980s. With population growth estimated at 2.8 percent throughout these two and a half decades, the trend in the per capita GDP has been at best stagnant and probably declining.

TABLE 22 **Senegal** Estimated **Real** Annual Grwth in GDP, Totl ad by Sector

|Sector|1960<br>708|197080|9<br>8<sup>9</sup>|1987b|
|---|---|---|---|---|
|Primary|3.0|-0.4|2.7|2.8|
|Secondary|4.2|2.7|4.2|73|
|Tertiary|1.7|13|2VI|2.7|
|Total|25|1.9|3.1|4.0|

Note: Based on series in 1979 constant prices Source: "World Bank, SeneuaL Country Econonic Memorandum 1984, p. 4; World Bank, SenefaL An EconoMy Under Adjustment. 1987. bIMF, Senezal - Statistical Anne' 1988, estimates. P Most of the decline in the real growth of GDP came in the second half of the 1970s **.** As a result of severe droughts in 19771978 and decliningproduction incentives, net output in the primary sector declined by an average of 3.9 percent per year from 1975 to 1980. Growth of output in the secondary sector also slowed down to 2.2 percent per annum during the latter half of the 1970s, down from an average growth of 3.2 percent per annum in the first half of the 1970s and 4.2 percent per year during the 1960s. Only the service sector (which includes public administration) grew at a faster rate in 1975-1980 than in 19701975.

The growth of the public (government) sector in 1975-80 has been steady, with the share of government consumption in final demand rising from 14.9 percent in 1970 to 19.8 percent in 1983. A particularly rapid increase occurred between 1975 and 1980, when the share jumped from 15.2 to 22.4 percent. The growth of public expenditures during this period is explained partially by the