rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

Agricultural innovation is necessary to accelerate growth and achieve food security in Africa (Hazell, 2013). Despite availability of yield-enhancing technologies, adoption rates in Sub-Saharan agriculture remain low (Gollin et al., 2005). A growing literature identies information failures as an impediment to the technological diusion process in agriculture (Bandiera and Rasul, 2006; Conley and Udry, 2010; Munshi, 2004). Less documented are the modalities through which information can best diuse and boost adoption of productive farming practices.

Agricultural extension services are designed to facilitate the diusion of innovations from lab to farm. In developing countries, they account for large shares of government expenditures on agriculture (Akroyd and Smith, 2007). These substantive investments are seldom supported by causal evidence regarding their eectiveness as a whole, or of a particular modality (Anderson and Feder, 2004). Contact farmers (CFs), who serve as points of contact between extension agents (EAs) and other farmers, are ubiquitously used as messengers of information in developing countries. Ecacy of the CF modality rests on two key assumptions. First, EAs will eectively train CFs to adopt and demonstrate new technologies to peers. Frequent EA visits are supposed to elicit a process of experiential learning among CFs. Second, other farmers' exposure to CFs will encourage wider adoption in the community, through a peer learning process. Despite some evidence of implementation and accountability constraints, and perhaps for lack of a viable policy alternative, the CF model persists across Africa (Gautam, 2000). Formally documenting returns to additional low-cost, scalable interventions to help leverage these large investments in agricultural extension services could signicantly aect the path of technology diusion.

We exploit a large-scale, government-run randomized controlled trial (RCT) to measure the impact of augmenting the CF model with a direct training on the diusion of a new technology in central Mozambique. Our treatment consists of adding a direct CF training to an existing CF model, holding everything else constant. In practice, CFs in treatment villages receive two three-day trainings (one in 2010, one in 2012) on a yield-enhancing technology at district headquarters, by the same experts and using the same curriculum as provided to EAs. All EAs were trained on sustainable land management (SLM) and were expected to train their local CFs to demonstrate the technology to other farmers in 200 villages. All CFs were provided demonstration kits to encourage 2
|Table 9<br>Estimated parametric adjustm<br>Tariff leveleffect estimation|ent functions<br>White collar<br>adjustment|Blue collar<br>adjustment|Capital<br>adjustment|
|---|---|---|---|
|Constant|0.052|-0.181|0.057|
||[0.051]|[0.047]***|[0.051]|
|Pos Shortage|0.289|0.778|0.192|
||[0.080]***|[0.071]***|[0.074]***|
|(ShortageW)^2|0.207|0.022|0.009|
||[0.028]***|[0.012]*|[0.013]|
|(ShortageW)^2*Pos|-0.136|||
||[0.044]***|||
|(ShortageB)^2|0.002|0.177|-0.022|
||[0.015]|[0.031]***|[0.015]|
|(ShortageB)^2*Pos||-0.083||
|||[0.047]*||
|(ShortageK)^2|-0.008|0.026|0.119|
||[0.013]|[0.011]**|[0.024]***|
|(ShortageK)^2*Pos|||-0.102|
||||[0.035]***|
|(ShortageW)*(ShortageB)|-0.047|-0.012|-0.007|
||[0.015]***|[0.013]|[0.017]|
|(ShortageW)*(ShortageK)|-0.022|-0.010|0.001|
||[0.013]|[0.015]|[0.013]|
|(ShortageB)*(ShortageK)|0.030|-0.056|-0.052|
||[0.017]*|[0.013]***|[0.01193]***|
|Constant*Tariff|0.00074|0.01419|0.002|
||[0.002]|[0.002]***|[0.002]|
|Pos Shortage*Tariff|-0.006|-0.026|-0.008|
||[0.003]**|[0.002]***|[0.002]***|
|(ShortageW)^2*Tariff|-0.001|||
||[0.001]|||
|(ShortageW)^2*Pos*Tariff|0.002|||
||[0.002]|||
|(ShortageB)^2*Tariff||-0.003||
|||[0.001]***||
|(ShortageB)^2*Pos*Tariff||0.002||
|||[0.002]||
|(ShortageK)^2*Tariff|||0.001|
||||[0.001]*|
|(ShortageK)^2*Pos*Tariff|||-0.000|
||||<br>[0.001]|
|Observations|4507|4507|4507|
|Number of id|627|627|627|
|<br>R-squared|0.32|0.34|<br>0.32|
|Note: Pos Shortage: Dummy=<br>ShortageK are the shortages f|1 if Shortage is p<br>or white collar. bl|ositive. Shortag<br>ue collar and ca|eW. ShortageB.<br>pital. Tariff is|
|<br>the sector average import tariff<br>10%;**significant at 5%;***s|<br>. Standard errors<br>ignificant at1%|<br>in brackets. * s|<br>ignificant at|

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Following the approach and specification in (3), we regress the share of the j-th decile in country i, θ _ij_ (which is defined as the ratio between the absolute income of the j- th decile,( _yijt_ ), and the sample mean income, ( _~~m~~ it_ ) on _TARi_ , _t_ − 5<sup>,</sup> the same set of relative endowments ( _`REit`_ ), their interaction with _TARi_ , _t_ − 5<sup>,and a set of controls (</sup><sup>_`Zit`_). As before, (4)</sup> includes a set of country dummies and dummies to control for the source of inequality data leading to the following equation to be estimated for each decile: Table 6 reports the results for the bottom three and top three deciles (cf. full results in Table A11).

Besides plausible estimates with the FE estimator (Milanovic (2005, footnote 8 argues that because this panel is very short there is insufficient data variability to use such an estimator), the following patterns stand out. First, the previous results are quite robust to the change of sample and to the use of decile data instead of the Gini coefficient. Except for the coefficient for the interaction term between tariffs and raw materials (which now turns out to be insignificant), all other coefficients related to tariffs have the same sign as in Table 4 and are significant. In particular, a reduction in protection is associated with increases in inequality since it reduces (increases) the will convince those who are skeptical of the idea that average growth reaches the poor, there is little choice but to use the surveys”, p.18. See Deaton (2005) for a deeper discussion on this issue. In our sample the correlation between _gH_ and _gPPP_ is ρ = 0.2917 , and a regression on annual income growth (over 1988 and 1998) measured from the surveys, _gH_ , and from national income, _gPPP_ is (std. errors in parenthesis): 20
periods. Indeed, while the transitions into and out of poverty almost balance out in the total— which is already reflected in the net poverty reduction of 1 percent from the cross sectional estimates shown in Table 1—the conditional probabilities (column 4) tell a different and more positive story where poor households have a larger chance of escaping poverty (45 percent) than that of non-poor households falling back into poverty (40 percent). This result is due to the fact that the proportion of the population that was poor in 2006 was smaller than the proportion that was not poor in the same year; consequently, the same level of transition out of or into poverty in absolute terms would translate into a higher figure in relative terms for the former population group (i.e., the average household that was poor in 2006 would have a higher chance of escaping poverty five years later).

For a comparative analysis, this probability of moving out of poverty is comparable to that of another developing country at a different time interval, Lao PDR during 2002/03-2007/08 (Dang and Lanjouw, 2013), and less than that of Bhutan around a similar time interval during 20072012 (World Bank, 2014c).<sup>16</sup> The corresponding figures for these countries are respectively 50 percent and 56 percent. However, both Bhutan and Lao PDR enjoyed a faster economic growth during these time intervals (approximately 7 percent and 5 percent annual per capita growth, respectively), thus the probability of falling back into poverty for these countries was much lower at respectively 5 percent and 16 percent. But regardless of the comparison, the seemingly unchanged poverty figures estimated from the cross sections for Senegal have clearly masked a high degree of poverty mobility in this country as revealed by the synthetic panels.

# **IV.2. Poverty Profiling for Population Groups**

> 16 The average GDP per capita in US dollars in constant prices in 2005 is $1,734, $465, and $789 respectively for Bhutan (during 2007-2012), Lao PDR (during 2002-2007), and Senegal (during 2006-2011) (World Bank, 2014a).

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# Figure 12: Long-run Growth Effects

<!-- Start of picture text --> 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Year<br>Simul Both No g_h No g_A Neither<br>30000<br>20000<br>10000<br>0<br><!-- End of picture text --> Note (1) Each line represents the actual or the predicted path of GDP per capita using different calibration methods.

Note (2) ”Simul”: Predicted GDP per capita calibrating at fully time-varying parameters, ”Both”: Predicted GDP per capita calibrating at constant fundamental parameters of human capital and labor-augmenting productivity growth, ”No g ~~h~~ ”: Predicted GDP per capita with no human capital growth, ”No g ~~A~~ ”: Predicted GDP per capita with no labor-augmenting productivity growth, ”Neither”: Predicted GDP per capita with neither human capital nor labor-augmenting productivity growth.

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Gourieroux, Christian. 2000. _Econometrics of Qualitative Dependent Variables._ Cambridge University Press, Cambridge, UK.

Greenwood, M. J.. 1997. “Internal Migration in Developed Countries.” In Rosenzweig and Stark Eds. _Handbook of Families and Population Economics_ . North-Holland Publisher, Amsterdam. Holland.

Hanson, Gordon. 2005. Globalization, Labor Income and Poverty in Mexico in Ann Harrison, ed., Globalization and Poverty, Chicago: University of Chicago Press and the National Bureau of Economic Research, forthcoming.

Hanson, Gordon. 1997. “Increasing Returns, Trade, and the Regional Structure of Wages.” _Economic Journal_ 107: 113-133.

Hanson, Gordon and Christopher Woodruff. 2003. “Emigration and Educational Attainment in Mexico.” Mimeographed. University of California at San Diego, CA, USA.

Hanson, Gordon and Antonio Spillembergo. 1999. “Illegal Immigration, Border Enforcement, and Relative Wages: Evidence from Apprehensions at the U.S.-Mexico Border.” _American Economic Review_ 89: 1337-1357.

Herzog, H.W. Jr., A.M. Schlottmann and T.P. Boehm. 1993. “Migration as Spatial Job Search: A Survey of Empirical Findings.” _Regional Studies_ 27: 605-620.

Jones, Richard C. 2001. “Maquiladoras and U. S.-Bound Migration in Central Mexico.” _Growth and Change_ 32: 193-216.

Leonard, J.S. and R. McCulloch. 1991. “Foreign-Owned Businesses in the United State.” In J.M. Abowd and R.B. Freeman, eds. _Immigration, Trade and the Labor Market._ NBER Project Report, The University of Chicago Press, IL, USA.

López and Schiff. 1998. “Migration and the Skill Composition of the Labour Force: The impact of Trade Liberalization in LDCs.” The Canadian Journal of Economics, 31:318336.

Lucas, R.E.B. 1997. “Internal Migration in Developing Countries,” In Rosenzweig and Stark, eds. _Handbook of Families and Population Economics_ . North-Holland, Amsterdam.

Markusen, J.R. and S. Zahniser. 1999. “Liberalization and incentives for labor migration: theory with applications to NAFTA.” In R. Faini, J. De Melo and K.F. Zimmermann, eds. _Migration. The Controversies and the Evidence._ Cambridge University Press, Cambridge, UK.

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The calculated environmental losses are provided in Table 4.

**Table 4: Estimated Unrealized Environmental Benefits from Wind Generation Losses in Selected Regions, 2009–2010**

||**Unrea**<br>**co**<br>**(US**|**lized potential**<br>**al saving**<br>**$ millions)**|**U**<br>**environ**<br>**(US**|**nrealized**<br>**mental benefits**<br>**$ millions)**|**Unrea**<br>**b**<br>**(US**|**lized climate**<br>**enefits**<br>**$ millions)**|
|---|---|---|---|---|---|---|
||2009|2010|2009|2010|2009|2010|
|**Inner**<br>**Mongolia **|64.2|135.8|72.97|154.38|17.91|37.89|
|**West**|47.4|98.7|53.93|112.20|13.24|27.54|
|**East**|16.7|37.1|19.03|42.18|4.67|10.35|
|**Jilin**|6.3|16.8|7.13|19.10|1.75|4.69|
|**Gansu**|5.8|8.0|6.65|9.11|1.63|2.24|
|**Heilongjiang**|3.7|10.7|4.15|12.20|1.02|2.99|
|**Liaoning**|0.7|4.1|0.85|4.70|0.21|1.15|
|**Hebei**|8.5|3.9|9.70|4.48|2.38|1.10|
|**Others**|0|0|0.00|0.00|0.00|0.00|
|**Total**|89.2|179.4|101.40|203.98|24.89|50.07|

# **_Lack of Transmission Capacity_**

26. The regional and provincial grids failed to accommodate the rapid development of renewable energy and wind in particular. Access of renewable energy or wind-based electricity to the grid was constrained for recognized, but not comprehensively addressed, technical, financial, and institutional reasons. In Inner Mongolia by the end of 2010, for example, 60 wind farms were developed with an installed capacity (7,000 MW) that is more than 12 percent of its total power generation capacity (64,600 MW) (see Figure 3). This resulted in serious problems of wind power evacuation and system balancing because Inner Mongolia’s electricity market was incapable of absorbing all generated electricity. Delivering electricity to the nearby North China grid is the only option for accommodating all generation during high wind periods. However, there are currently only two 500 kV lines, built during the 1990s, that are capable of delivering 4,300 MW from Inner Mongolia to the North China grid. During the daytime, the evacuation of wind capacity is not a serious issue. However, during winter nights, electricity generated by CHP (Combined Heat and Power Generation) plants has to be dispatched first to meet the high, space-heating demand. Since wind usually peaks during nighttime winter hours, wind electricity generated during these hours is unlikely to be dispatched because of grid constraints. Evacuation of electricity during high wind hours will, therefore, require building new or strengthening existing transmission lines. Expected wind development in Inner Mongolia requires major strengthening of the transmission system 10
- disability status (see, e.g., Mont and Nguyen (2011) for Vietnam; Mitra, Posarac, and

- Vick (2013) for a multi-country study)

- unexpected shocks (see, e.g., Dercon (2004) for Ethiopia), and

- geographical isolation/ remoteness or lack of community infrastructure (see, e.g., Bloom,

- Canning, and Sevilla (2003) and Sachs et al. (2004) for multi-country studies), and

- migration (see, e.g., Christiaensen and Todo (2014) for a multi-country study).

These characteristics are also generally consistent with the findings in recent review studies on poverty mobility by Dercon and Shapiro (2007) and Baulch (2011). We examine separately the bivariate relationship between some major correlates and poverty dynamics before considering them all together in an attempt to provide some rough ordering of the importance of these correlates on poverty mobility in the next section.

# **IV.2.2. Poverty Profiling**

# **Residence Area**

The relationship between residence area and poverty mobility is graphically illustrated in Figure 2, where Panel A and Panel B show poverty movements conditional on households being poor in the first period and non-poor in the second period, respectively. The bar charts then indicate the breakdown of the transitions that are separately categorized by urban areas and rural areas. Figure 2 thus shows a much larger proportion (around 56 percent) of households moving out of poverty in urban areas than in rural areas, which is around 16 percent more as indicated by the yellow bars in Panel A. Equivalently, as earlier discussed with equation (6), the factors that are positively associated with upward mobility are by definition negatively associated with chronic poverty, and vice versa. Thus an alternative way to interpret the same result that is also borne out graphically is that a much smaller proportion of households remained in poverty in 19
**Table 5: Robustness of Maternal Mortality Estimates to Different Coefficients**

Change in maternal mortality due to health care worker deaths from Ebola Guinea Liberia Sierra Leone <u>Method 1: Speybroeck et al. (2006) aggregated doctors and nurses, controlling for GDP per capita, income</u> poverty, female literacy (from Table 2) 38% 111% 74% <u>Method 2: Speybroeck et al. (2006) disaggregated doctors and nurses, plus interaction between doctors & nurses,</u> controlling for GDP per capita, income poverty, female literacy 27% 73% 49% <u>Method 3: Anand and Bärninghausen (2004) aggregated doctors and nurses, controlling for GNI per capita,</u> income poverty, female literacy 31% 92% 61% <u>Method 4: Anand and Bärninghausen (2004) disaggregated doctors and nurses, controlling for GNI per capita,</u> income poverty, female literacy 12% 23% 16% Note: Using coefficients from Robinson and Wharrad<sup><u>16</u></sup> yields smaller estimates of 5%, 3%, and 3% increases in maternal mortality for Guinea, Liberia, and Sierra Leone. However, this is explained by the fact that they only report a coefficient for doctors (although nurses are included in their specification, which also controls for GNP, female literacy, and births attended).

# **Discussion**

Taking the estimates from our preferred method (Method 1 in Table 5), the results presented here suggest that even if Ebola were eliminated today, the loss in doctors, nurses, and midwives that has occurred to date as a result of the epidemic could potentially lead to increases in maternal mortality of 38% in Guinea, 74% in Sierra Leone, and 111% in Liberia, relative to pre-Ebola rates. Combining this with data on the most recent rate of live births per 1,000 people and population numbers in each country pre-Ebola<sup><u>9</u></sup> implies that an additional 4,022 women would die per year in childbirth as a result of doctors, nurses, and midwives lost to Ebola. Of these, some 1,083 annual deaths would be in Guinea, 1,094 in Liberia, and 1,845 in Sierra Leone. This would bring the countries back to rates of maternal mortality last seen in 2000 in Guinea and Sierra Leone, and 1995 in Liberia.<sup><u>9</u></sup> It is important to keep in mind that these are estimates of the direct effect of losing health personnel; these estimates do not take into account potential indirect effects such as lower health system utilization due to fear of contracting Ebola.

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for skills, in particular in the view to export its human capital. Although the Philippines’ strategy presents its own difficulties and specificities, these comparisons suggest that there is a potentially unmet demand for skills in Vietnam.

# _The return to higher educational attainment_

There are important returns to higher education in terms of activity rates and type of employment. The patterns of labor force status by educational attainment (Table 7) suggest that college educated individuals are the most likely to participate in the labor force: they have the lowest inactivity rate, but also the highest unemployment rate. Workers with higher education are more likely to work in wage employment than workers with lower levels of educational attainment. University graduates and primary school graduates have the second highest labor force participation; only about 15 percent of these two groups are inactive. However, the similarities between the two groups end here: while more than 73 percent of primary school graduates are self-employed, more than 90 percent of University graduates are wage workers, including 66 percent of University graduate working in the state-owned sector (Table 8). This is remarkable since the latter sector includes only about 10 percent of employment. In fact, up to lower secondary schooling, over 70 percent of workers are self-employed; this share falls to about 48 percent for upper secondary education, about 14 percent for college education, and less than 9 percent for University education (Table 8).

**Table 7: Educational attainment and activity status**

|Activity status|No degree|Primary<br>school|Lower secondary<br>school|Upper secondary<br>school|College|University|Total|
|---|---|---|---|---|---|---|---|
|Work|69.3|84.8|75.6|64.2|88.3|83.5|74.7|
|Unemployed|0.2|0.2|0.2|0.5|1.2|0.3|0.3|
|Inactive|30.5|15.0|24.2|35.3|10.5|16.2|25.1|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

_Source: VHLSS 2008_

**Table 8: Composition of employment by educational status (%)**

||No<br>degree|Primary<br>school|Lower<br>secondary<br>school|Upper<br>secondary<br>school|College|University|Total|
|---|---|---|---|---|---|---|---|
|SELF-EMPLOYED/Private sector|0.2|0.3|0.5|1.2|0.3|0.6|0.5|
|Self-employed excluding private sector|77.0|73.1|70.8|47.9|13.8|8.6|65.3|
|OTHER HOUSEHOLDS|20.2|18.9|16.1|11.8|4.0|1.8|16.1|

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- 33 -

upon the poverty measure used. When the United Nations Development Program's poverty line is used to measure poverty, a significant inverted U relationship is found which peaks at a poverty incidence of about 17 percent (about one half the sample mean incidence). However, when any of the other estimates of the incidence of poverty are substituted for PCOM in the regressions, none of the poverty coefficient estimates is significantly different from zero. Further, the signs of the estimated coefficients are unstable. For WBPOOR and P2085, the coefficient signs indicate an inverted U relationship, while for P3085 and P6085, a U shaped relationship is implied. Finally, when any of the measures of poverty incidence is substituted for poverty commitment, the significance levels of the other regressors are affected as are the coefficient magnitudes; just which coefficient estimates are affected differs across the alternative measures of poverty. These results indicate that estimates of the incidence of poverty cannot be used as a proxy for a government's commitment to poverty alleviation.

Government objectives do influence infrastructure expenditures as the results for poverty commitment show. Appropriate indices of government objectives need to be developed to fully explain expenditure patterns. Further work to define and refine indicators along the lines of our PCOM variable are warranted.

# **_15._** _Central Budget Per Capita Infrastructure Expenditures: Summary and Conclusions_

Table 8 summarizes the overall results of the analysis. The table shows the hypothesized relationship between the different variables and per capita central government expenditures, and the direction of the estimated relationship. The instantaneous rate of change in infrastructure expenditures and the elasticity are shown as computed for the sample mean values of the independent and dependent variables. In the case of nonlinear relationships, the estimated peak or trough is indicated. The values without parentheses are those corresponding to Table 6's FCINFPA base regression including foreign funding, M2, (and, except where relevant, excluding the mix of foreign funding), while the values in parentheses are those corresponding to the comparable regression including the dummy variables for year (Table 7).