rafmacalaba/datause-encoder-data huggingface dataset:
adoption and diusion of information to other farmers. We augmented the CF model by centrally training CFs on SLM in 150 randomly selected (treatment) communities. The training format was part lectures, part hands-on, with similar content and breadth as the EA training. The central training is the only dierence between treatment and control, and all 200 villages adhere to the status quo CF model. We use two rounds of follow-up survey data on 200 CFs and a random sample of over 5,000 other farmers to examine the impact of adding a central training on knowledge and adoption of the technology, as well as agricultural production.

The CF model enables a process of experiential learning among CFs through the use of demonstration activities and regular on-site feedback from EAs. This practice is similar to on-the-job, learning-by-doing processes in other labor markets. Neoclassical growth theories suggest learningby-doing may be of equal importance to formal training in explaining human capital formation as a production input (Lucas, 1988). While learning-by-doing theories are supported in the context of rm or plant-level studies (Levitt et al., 2012; Thompson, 2010), empirical evidence of the signicance of learning through extension programs on agricultural growth is mixed (Bindlish and Evenson, 1997; Purcell and Anderson, 1997; Gautam, 2000; Anderson and Feder, 2007; Benin et al., 2007; Davis et al., 2012; Waddington et al., 2014). We contribute to this research agenda by formally documenting the impact of augmenting an existing, decentralized extension model with a relatively low-cost centralized training modality.

Adding a direct training may aect technology adoption among CFs through three broad categories of mechanisms: increased quantity of information, enhanced learning experience, and channels other than knowledge. First, the curriculum in a direct training may increase the quantity of information transferred (e.g., number of techniques taught). Central trainings are oered in an enclosed setting under the supervision of project sta, and extension agents present the material from the course manual. This plausibly increases the chance that the intended curriculum is covered.

Second, the centralized format of the training may enhance the learning experience. For instance, the formal setting may add credibility to the information. While the use of course materials hardly aects learning indicators in other settings (Tan et al., 1999; Glewwe et al., 2004, 2009), use of computing technology as a complementary input to a standard curriculum has been shown to have a positive eect on learning (Banerjee et al., 2007; Linden, 2008). For these reasons, the information set shared during a training held at district headquarters may be (perceived to be) of higher quality 3
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Policy Research Working Paper 10480

# **Abstract**

The paper documents the evolution of territorial disparities in labor and location productivity in 14 countries in Latin America, using millions of observations from harmonized household surveys and censuses. Between the early 2000s and the late 2010s, most countries in the region experienced significant reductions in regional inequality as real labor incomes and location productivity premia converged at the first and second administrative levels. The leveling up reflected both the slowdown in productivity growth in affluent predominantly urban municipalities and the catchup of relatively poor, predominantly rural municipalities. Absolute convergence narrowed the labor income gaps with leading metropolitan areas, including the disparitites exploitable through migration, especially among the bottom 40 percent of households, as cities de-industrialized, yet continued to attract migrants. On the eve of the Covid19 pandemic, income disparities with leading metropolitan areas remained high in nearly all countries, largely due to differences in educational attainment, but in a few countries, large differences in returns to endowments indicate potentially significant returns to migration to the leading metropolitan areas, especially for residents of relatively poor, remote regions. Rather than a clear rural-urban-metropolitan divide, in most countries the paper documents substantial overlap between the location-premia distributions of different types of second-level administrative areas and small differences between the average urban and rural place productivity premia.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at odaoust@worldbank.org, vgaldo@worldbank.org, or eianchovichina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
reversed and the share of traded goods will eventually increase when there are significant productivity and output growth in those sectors.

**Table 15** : **Shares of real GDP value added by type of sectors**

|||**Initial Year**<br>**Inc**|**of Exogenous**<br>**ome**|**Terminal Yea**<br>**Inc**|**r of Exogenous**<br>**ome**|
|---|---|---|---|---|---|
||Base|PIH with<br>Public<br>Investment -<br>Fixed Capital|PIH with Public<br>Investment -<br>Mobile Capital|PIH with Public<br>Investment -<br>Fixed Capital|PIH with Public<br>Investment -<br>Mobile Capital|
|Traded Good|8.16|7.17|6.64|10.94|11.07|
|Semi-Traded Good|46.60|46.41|45.72|45.48|45.56|
|Non-traded Good|45.23|46.43|47.64|43.58|43.37|

_Source:_ Model simulations.

_Note_ : To distribute the sectors into the three broad goods in the table, we assume the following classifications: traded goods = mining which is exported only, there are no imports; semi-traded goods = food agriculture, other agriculture, food manufacturing, other manufacturing, there are both imports and exports; and non-traded goods = construction, services, public services, and education/health, there are no imports or exports (with the exception of services which has a very small share of consumption imported and a very small share of production exported).

# **_Real Exchange Effects of Alternative Spending Strategy_**

In what follows, we assume that capital is fixed once installed as this is more realistic for a developing country like Niger. New investment is allocated each period based on the relative rate of returns of each sector.

Figure 5 (Panels A, B, C, and D) shows the real exchange effects of each spending strategy. For each strategy, we compare the case in which exogenous mineral export income is used mainly for consumption and the case in which exogenous mineral export income is used to raise human and public capital. In the consumption case, households receive the mineral revenue through lump-sum transfers from government and spend it primarily for consumption, saving a small portion for investment. We further assume that consumption and investment by households in a subsistence economy are diffused and do not raise productivity very much. To be sure, the assumption is made mainly for simplicity and to distinguish the effects of consumption and investment.

27
Table 1: Summary of Infrastructure data

|Infrastructure|Country|Year|Source|
|---|---|---|---|
||Nigeria<br>Nigeria|1991<br>2009<br>around 2013|Jedwab and Storeygard (2020)<br>Foster and Briceno-Garmendia (2010)<br>Ali et al. (2015)|
|Roads|Cameroon|2009|Foster and Briceno-Garmendia (2010)|
|||2018|Road authorities|
||Chad|2009<br>2018|Foster and Briceno-Garmendia (2010)<br>Road authorities|
|Electricity|All|varyacross countries|Nighttime lights / Population raster|
|Electricity grid|All<br>All|around 2006<br>most recent|Foster and Briceno-Garmendia (2010)<br>gridfnder.organd Arderne et al.(2020)|
|Internet|All|2009-2019|Africa Bandwidth Maps 2009-19|

We gathered geospatial maps of road expansion using government sources as well as previously harmonized collections of road networks (Foster and Briceno-Garmendia, 2010; Jedwab and Storeygard, 2020). The quality of the network and associated features and the frequency of updates vary across countries. We first used road networks from Foster and Briceno-Garmendia (2010) which relied on surveys and governments sources as a baseline for years between 2000 and 2007. Additional government surveys provided more recent road networks for Cameroon and Chad. We used the road network from Ali et al. (2015) for Nigeria which included road survey data from the Nigeria Federal Roads Maintenance Agency (FERMA) and the World Bank’s Fadama project.<sup>4</sup> Panels of roads from the same source are rare. Related works include a similar paper applied to the Horn of Africa (Herrera Dappe and Lebrand, 2021) and Moneke (2020) whose focus on ‘all-weather’ (i.e. gravel, asphalt or bitumen surface) roads is closer to ours.

We used two methods to map access to the electricity network: nighttime lights and maps of power transmission grids. Nighttime light data are available for most years and locations but convey imperfect information on electrification. Historical maps of electricity grids are more difficult to find and use in a consistent way. We used satellite images of annualized nighttime lights (VIIRS for 2016, DMSP for 1992-2013) and population rasters from World Pop to calculate the percentage of the population that was electrified (lived in settlements that produce some light at night). We compared the results for two metrics: a dummy that is equal to one if at least 50 percent of the population has access to electricity and the share of the electrified population per district. Such methods have been used before to estimate electricity access in remote areas and guide grid extension programs.<sup>5</sup> This method assumes that locations that emit lights at night are in settlements that have electricity access and that their electricity is

> 4To "ground truth" and take advantage of first hand local knowledge, Ali et al. (2015) detail how government offices across Nigeria were surveyed about the conditions of specific road segments near them.

> 5An example of mapping rural electrification based on night-time lights can be found at http://india.nightlights.io/.

6
Table 1 shows a positive and strong correlation between our measure of factor endowments and educational achievement, as measured by the aforementioned Index of Knowledge Distribution, over a long period of history between 1858 and 1998. Countries with a higher wheat-sugar ratio of arable land have had a higher percentage of students and literates throughout this period (measured with 10-year intervals). Tables 2A and 2B show strongly positive correlations between educational achievement during the period 1858-1998 (measured once every 10 years) and our measures of equality of opportunity (HOI and D-Index) using PISA data from 2009. Thus unequal educational opportunities appear to persist over time, all the way to our current measures of unequal opportunities. Taken together, the two types of correlation in Tables 1 and 2 seem to support the use of our measure of factor endowments, the wheatsugar ratio of arable land, as an instrument for educational opportunities.

<Tables 1, 2A and 2B here>

# **4. Empirical results**

We start by showing the strong correlations between our measures of Equality of Opportunity and GDP per capita. The HOI and D-Index (derived from PISA test scores or timely completion of primary school) have strong positive and negative correlation, respectively, with the log of GDP per capita (Figure 3) – consistent with our hypothesis. The correlations with GDP are weaker for measures derived from school attendance (10-14 years) and starting primary school on time (6-7 years) (Figure 4).<sup>14</sup>

## **Figure 3**

<!-- Start of picture text --> Opportunities and Economic Development Using PISA Test Scores<br>r = 0.766*** r = -0.713***<br>0 20 40 60 80 100 0 10 20 30 40<br>Human Opportunity Index - PISA Test Scores Dissimilarity Index - PISA Test Scores<br>Fitted values Americas<br>East Asia, South Asia & Pacific Europe & Central Asia<br>Middle East and Africa<br>These graphs show the strong economical and statistical correlation (p-value is equal to zero in both cases)<br>between our measures of equality of opportunity and economic development<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>Logaritm of GDP pc at Current USD (2005-2010) Logaritm of GDP pc at Current USD (2005-2010)<br><!-- End of picture text -->

> 14 See Appendix 2, Figures A-2.1 and A-2.2.

11
**Table 6: Inefficiency Parameters from Non-Neutral Stochastic Frontier Translog Production Function**

||**Marginal**<br>**Effect**|**Interaction with**<br>**Transplant**<br>**Adoption**|
|---|---|---|
|**Inefficiency Parameters**|||
|Transplanting Technique Used|0.507***||
||(0.000)||
|Impulsiveness|0.058<br>|-0.349<br>|
||(0.424)<br>|(0.357)|
|Tenacity|-0.066<br>|0.190<br>|
||(0.517)|(0.539)|
|Polychronicity|-0.208***|0.291|
||(0.002)|(0.228)|
|Internal Locus|0.101|-0.356|
||(0.181)|(0.197)|
|Achievement Orientation|-0.260**|-0.870**|
||(0.030)|(0.032)|
|Power Motivation|-0.056***|-0.539**|
||(0.009)|(0.028)|
|Organization|0.158|0.179|
||(0.411)|(0.527)|
|Optimism|-0.128|-0.250|
||(0.590)|(0.446)|
|Work Centrality/Passion|-0.265**|0.477|
||(0.017)|(0.195)|
|Digitspan|-0.161**|-0.231|
||(0.025)|(0.133)|
|Weta Irrigation Scheme|1.184***|0.677|
||(0.000)|(0.553)|
|Primary Cultivator Age|-0.010|-0.029|
||(0.259)|(0.142)|
|Primary Cultivator Education|0.008|0.066|
||(0.236)|(0.129)|
|Primary Cultivator Experience|0.007|0.032|
||(0.444)|(0.236)|
|Female Primary Cultivator|0.305|0.248|
||(0.158)|(0.498)|
|No. HH Members (log)|0.458***|0.931**|
||(0.008)|(0.041)|
|**Tests for joint significance**|||
|All Interactions with Transplant Adoption||0.000***|
|All Personality Traits|0.006***|0.065**|
|Standard Human Capital Traits|0.315|0.108|
|Motivational Traits Only|0.006***|0.018**|
|Number of Observations|1,778|1,778|
|/Usigma|-0.037|-0.091|
||0.880|0.698|
|/Vsigma|-2.835***|-2.797***|
||0.000|0.000|

_Note:_ Marginal effects are reported at the mean. Likelihood ratio p-values are shown in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Standard human capital traits are age, education and experience. Motivational traits are achievement motivation, power motivation, tenacity and work centrality/passion. Indicators of seed type, and parcel’s tenure status included but not reported.

23
in Uganda to 13.2 percent in Tanzania), while in urban areas, the gender gap has increased over time in Uganda (10.5 percent).

Next to having a main or secondary wage job, women and men also engage in casual wage labor or employment in a social safety net program. Casual wage labor is explicitly mentioned in the Ethiopia ESS and Malawi IHPS questionnaires, where it constitutes an important source of offfarm employment. According to the latest survey round, 4 percent of women and 11 percent of men in Ethiopia and 27 percent of women and 33 percent of men in Malawi participate in casual labor. This implies that people in Malawi participate more in casual wage labor (i.e. ganyu) than in main wage employment, and that this difference is higher for women (21 percentage points) than for men (12 percentage points). Given that ganyu is used as a last-resort income source rather than a regular source of income, the gender gap might indicate that women’s entry into employment is rather driven through push factors than pull factors, which might hamper their contribution to economic development and empowerment (Oya, 2013). Casual labor is more common in rural areas than in urban areas in Malawi, but similar across rural and urban areas in Ethiopia. Employment in a social safety net program is in general negligible across countries. It is somewhat common in rural Ethiopia, where 3 percent of women and 6 percent of men participate, and in Malawi where 7 percent of women and 5 percent of men participate. While these rates have remained the same over time in Ethiopia, we notice a sharp increase in Malawi, especially for women living in urban areas.

The gender gap in self-employment is smaller than in wage employment. Depending on the country, men are more likely to participate than women (21 percent versus 18 percent in Malawi), whether there is no difference (18 percent in Ethiopia, and 16 percent in Uganda), or whether women are more likely to participate (39 percent versus 35 percent in Nigeria, and 26 percent versus 25 percent in Tanzania). The higher prevalence of women’s ownership of NFEs in Nigeria is in line with other studies in rural West-Africa (Ackah, 2013). These trends are similar over time, and across rural and urban areas. For men, participation in wage employment is more common than in self-employment, except in Nigeria. For women, participation in self-employment is more 12
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