rafmacalaba/datause-encoder-data huggingface dataset:
hydrodynamic module, the DHI Global Tide model, the Bay of Bengal model, and the MIKE 11 advection-dispersion module were used to estimate the freshwater flows from upstream, surface water runoff from rainfall events, and tidal dynamics in the coastal river system. The models were also used to predict location-specific river salinity for 27 alternative scenarios of climate change<sup>47</sup> by December 2049 to May 2050. Fourth, predictions of location-specific concentrations of the salinity of the coastal river systems for alternative scenarios of climate change were compared with concentrations at the baseline (measured river salinity in 2012). Fifth, estimated geo-coded changes in river salinity were overlaid with district boundaries to quantify the expected changes by administrative boundaries.

Our findings indicate that climate change will cause _significant_ changes in river salinity in the southwest coastal area of Bangladesh by 2050. Overall, a probable reduction in slight saline (<1dS/m) river area from 22 to 13 percent, a reduction in slight to moderately saline (1–5dS/m) river area from 35 to 21 percent, an increase in moderate to high saline (5–10dS/m) river area from 8 to 27 percent, and an increase in highly saline (> 10dS/m) river area from 35 to 40 percent is expected.<sup>48</sup> These changes are likely to lead to significant shortages of drinking water in the coastal urban areas; scarcity of water for irrigation for dry-season agriculture, especially for the _boro_ crop; and significant changes in the coastal aquatic ecosystems as well as in the World Heritage Sunderbans mangrove forest. In particular, our estimates indicate that freshwater river area (0–1ppt) may decrease from 40.8 to 17.1 percent and river area suitable for agricultural irrigation (with salinity less than 2ppt) may decrease by 29.7 percent. Changes in the availability of freshwater fish will likely affect the composition of capture fishery. Wild habitats of freshwater fish (including small indigenous species) and giant freshwater prawn (Golda) will be affected adversely. But the increase in brackish water will enhance opportunities for brackish water aquaculture, for example, farming of _Tilapia Nilotica_ and Black Tiger Shrimp (Bagda) in coastal districts. Climate change is projected to cause an increase in water salinity that is also likely to induce an overall shift from Sundari (the species with the highest market value) to Gewa and Guran in Sunderbans.

> 47 Approximated by combinations of three emission scenarios (B1, A1b, and A2), temperature and rainfall projections from three GCMs (IPS-CM4L, MIROC3.2, and ECHO-G), and alternative estimates of RMSLR.

> 48 Water with 1.15 deciSiemens per water (dS/m) contains approximately 2,000 pounds of salt for every acre foot of water.

32
# IV.Crop Productivity

## A. Regional Variation in Productivity

The Blue and White Nile regions tend to have the highest yields for three of the four major crops produced (Figure 33)—sorghum, sesame, and groundnut. The millet yield is highest in the Darfur region, where it is predominantly grown. Though sesame is predominantly grown in Kordofan, the region has very low yields of the crop. Kordofan also has the lowest yields of sorghum and groundnut.

Figure 33: Yield of the Four Major Cultivated Crops in Sudan (in Relevant Growing Regions), 2014/15

### (a) Sorghum

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>(c) Groundnut<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Eastern Blue & Kordufan Darfur Sudan<br>White<br>Nile<br>Yield (100 kg/acre)<br>Yield (100 kg/acre)<br><!-- End of picture text -->

### (b) Millet

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Blue & Kordufan Darfur Sudan<br>White Nile<br>Yield (100 kg/acre)<br><!-- End of picture text -->

### (d) Sesame

<!-- Start of picture text --> 0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Eastern Blue & Kordufan Darfur Sudan<br>White<br>Nile<br>Yield (100 kg/acre)<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

36
- 4 -

The remaining sections are organized as follows: Section II develops our empirical strategy and describes our data, including key transport connectivity variables. Section III discusses the main estimation results and some policy implications. Then, Section IV concludes.

# **II. EMPIRICAL MODEL AND DATA**

Following the literature (e.g., Gyimah-Brempong, 1987; Bravo-Ortega and Lederman, 2004), a simple production function is considered with transport connectivity included as one of the production inputs:

# where _Y_ is the volume of rice produced in location _i_ .

Our primary source of data is the spatial production allocation model (SPAM) developed by the International Food Policy Research Institute (IFPRI) for generating geographically highly disaggregated crop production data.<sup>1</sup> The SPAM is a spatial model to allocate crop production derived from large statistics reporting units, such as country, province and district, to a raster grid at a spatial resolution of 5 minutes of arc (approximately, at a resolution of 10km x10km pixel). See You and Wood (2006) and You _et al._ (2009) for details.

While the model covers 42 crops, the current analysis is only focused on rice, which is the most common and important crop in Madagascar. The country’s total rice production amounts to about 4 million tons or US$1 billion per annum ( **Figure 2** ). About 85 percent of farmers engage in rice production. Using the spatial distribution of rice production based on the 2010 SPAM, the latest district-level rice production data for the period of 2013-15 are disaggregated into 3,198 locations or pixels. This is our dependent variable, _Y_ ( **Figure 3** ).

> 1 It is available on the Internet at MapSpaM.info.
c. By categories of main income source (Each income source accounts for more than 70% of total household income) <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>Fell into poverty Fell out of poverty Stayed in poverty Stayed out of poverty<br>Dependence on: Wage/salary agr. income Dependence on: Self‐employed agr. Income<br>Dependence on: Wage/salary non‐agr. income Dependence on: Self‐employed non‐agr. Income<br>Dependence on: Non‐labor income<br>category<br>Likelihood of being in each poverty‐transition<br><!-- End of picture text --> Note: Other religions include Christian, Sikh and Jain. Of those who were poor in 2005, the distribution is: Forward castes: 9.3%; OBC: 33.7%; Dalit: 27.6%; Adivasi: 13.5%; Muslim: 15.1%; Christian, Sikh and Jain: 0.9%, for total of 100%. Non-labor income includes the following income categories: remittances, benefits and other income.

Source: Calculations based on IHDS, 2005 and 2012.

Compared to other caste/religion groups, upward mobility is low for Dalits and even lower for Adivasis; and conversely, downward mobility is the highest for Adivasis and the second-highest for Dalits.<sup>12</sup> Figure 3 shows that conditional on being poor in 2005, an Adivasi is 25 percent and a Dalit is 8 percent less likely to be non-poor in 2012 than an OBC (the group with the next lowest probability). And conditional on being nonpoor in 2005, an Adivasi and a Dalit are 12 and 5 percent more likely to be poor in 2012, respectively, than a Muslim (the group with the next highest probability). These results are qualitatively unchanged even after controlling for the effects of education, household composition (size and share of adults) and area of residence (urban/rural) in the year 2005.<sup>13</sup> Thus Adivasis are at a disadvantage relative to the rest of the population both in terms of chronic poverty and transitions in and out of poverty; while Dalits have a smaller disadvantage relative to 12 See also Thorat et al. (2014).

13 This refers to probit regressions of the likelihood of being non-poor in 2012 among those who were poor in 2005 and the likelihood of being poor in 2012 among those who were non-poor in 2005 (results available upon request). The independent variables include the maximum education of household members, household size and share of adults, area of residence (urban/rural) (all in 2005), and caste/religion. Being an Adivasi, Dalit or OBC has a significantly negative (positive) effect—in declining order of magnitude—on the likelihood of rising out of (falling into) poverty relative to forward castes; and being a Muslim has a positive and significant effect on falling into poverty only. Secondary and tertiary education, and household size have significant impacts on both likelihoods in the expected direction; share of adults has a positive association with the likelihood of rising out of poverty; and being urban reduces the likelihood of falling into poverty.

10
# **3 Time-series Trends of FCS and non-FCS Projects’ FY01-13**

**The size of projects, measured through net commitments and total disbursements, varies from year to year (see Figure 8).** When examining the size of projects by the fiscal year in which they closed, no obvious trends emerge for either FCS or non-FCS projects. However, using the more forward looking measure of net commitment in the year a project is approved, it appears that FCS projects are on average getting marginally smaller, while non-FCS projects are getting significantly larger.

**Projects in both FCS and non-FCS countries declined in average length from FY2001 to FY2010 from about 6 years to a little over 4 years and are now increasing in length.** In general projects in FCS countries tend to be around 6 months shorter than those in non-FCS countries.

**Total and relative preparation costs vary from year to year but both show a declining trend in FCS and non-FCS countries (see Figure 9).** FCS countries on average have lower preparation costs per project but as projects in FCS countries are generally smaller, this corresponds to larger preparation costs relative to net commitment.

**Total supervision costs in FCS and non-FCS countries are similar and increased between FY2001 to FY2012 but dropped in FY2013.** Relative supervision costs tend to be larger in FCS, given their smaller net commitments, and grew more substantially through to FY2012. However in the most recent year where data was available, FY2013, relative supervision costs had dropped substantially.

**Less staff time is spent supervising and preparing projects in FCS countries than in non-FCS countries (see Figure 10).** Almost 40% of staff time is provided by staff located in country offices for projects in non-FCS countries, while about 25% is provided by in country staff for FCS countries. The proportion of time spent on projects by GH or above level staff has experienced a small recent decline in FCS countries relative to equivalent measure in non-FCS countries.

**Mission travel to projects in FCS and non-FCS countries has stayed approximately constant since FY2005**

**(see Figure 11).** Consultant time spent on projects in FCS and non-FCS countries grew substantially between FY2001 to FY2007, but has since declined substantially. Consultant time has generally been slightly higher in FCS countries. Total time spent on projects, measured using staff time and consultant time, has declined in both countries, and has always been lower in FCS countries.

**The number of staff based in FCS countries has plateaued in recent years due to a decline in the number**

14
Due to seasonal variations in returns to labor in farm activities, individuals might work temporarily in the non-farm sector. Some household members might work off-farm if there are diminishing returns to labor in agricultural production. Simultaneous income generation from both farm and non-farm sectors can be a strategy to cope with income risk if appropriate insurance markets are not available. Engaging in activities independent from agriculture such as manufactures can counter farm-related risk. Households in Burkina Faso that diversified their income into non-farm activities were more able to cope with droughts during the 1980s (Webb and Reardon 1992). Being able to hedge against risk in this way may enable farmers to increase the adoption of more risky high-return crops (Lanjouw and Lanjouw 1995, 2001). Other market failures can also explain income diversification. If capital markets do not exist, households investing in farm equipment will engage in non-farm activities to raise the required capital. Nonfarm income in Burkina Faso increased farm productivity by stimulating the adoption of animal traction (Savadogo, Reardon and Pietola 1998).<sup>11</sup> Missing land markets might prevent individuals whose comparative advantage is in non-farm employment from fully specializing in these activities.

Rural non-farm employment is not an option for all rural households. Zezza et al. (2008), using survey data from 15 developing countries, identify patterns along demographic characteristics as well as asset possession of households by income source. They conclude that households are more likely to engage in on-farm work if they own land, have lower levels of education, have little access to infrastructure and a greater share of their labor force members is female. Households deriving income from non-agricultural wages are usually more educated and have better access to infrastructure. Households receiving income from remittances are more likely to have a female head. Reardon (1997), focusing on African countries, draws similar conclusions but stresses the fact that households with higher income from non-farm activities also have higher total income and larger landholdings. This raises the question of causality. Either income diversification into non-farm activities leads to higher income because they generate higher returns, or diversifying households are better endowed initially.

> 11 This argument could, however, also go the other way around. Instead of lacking credit supplies, wealthy farm households might not be able to find profitable saving opportunities in financial markets and therefore invest in rural enterprises (Lanjouw 2001).

28
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44
<!-- Start of picture text --> Percentage of Global Middle Class<br>100<br>80<br>Rest of the<br>World (56%)<br>Rest of the<br>60<br>World (87%)<br>40<br>China (38%)<br>20<br>India (6%)<br>China (13%)<br>0<br>2000 2030<br>Source: Authors' calculations<br><!-- End of picture text --> The importance of China and India in the global middle class will depend on their economic and population growth rates and the changes in their within-country income inequality. For instance, in China, 56 million people belonged to the global middle class in 2000—each of them earning more than 90 percent of all Chinese citizens, i.e. they belonged to the richest decile. By 2030, assuming income inequality in China remains constant, there will be 361 million Chinese in the global middle class, and their earnings will range from the sixth to the ninth decile of the Chinese national income distribution. Chinese members of the global middle class will no longer be among the richest Chinese citizens but will probably be considered upper middle class in their country. On the other hand, if China manages to reduce income disparities, making middle income cohorts fatter, they would contribute even further to the global middle class.

To inspect these effects in more detail, in Figure 13 we fit a non-parametric kernel income density for China, India and the world population in 2000 and 2030. Figure 13 consistently shows the proportion of world population to Chinese and Indian populations; hence the Chinese and Indian densities can be interpret as the probabilities of being within the different income ranges and being Chinese and Indian citizens, respectively. Several interesting features are highlighted by Figure 13. In 2000, the mode of the global income distribution (1993 PPP $114), i.e. the income value that more individuals in the world were receiving, was largely determined by the level earned by a high proportion of Indian upper middle class citizens and members of the Chinese lower middle class (overlapping of the Chinese and Indian income densities in Figure 13). Focusing on the country-specific distributions, we can see that in 2000 incomes were less skewed in India compared with China (India’s distribution had a larger density around the mean); in 2000, the Gini coefficient for India was equal to 0.29 compared with a value of 0.42 in China. In fact, due to the relatively unequal distribution in China, its richest citizens could be part of the global middle class in 2000. By year 2030, after several years of growth rates 21
|**Table 7: Being Hired**<br>Dependent variable: w|**by a Progra**<br>orkingina p<br>(1)|**m Firm: Prob**<br>rogram firm<br>(2)|**it Selection M**<br>(3)|**odels**<br>(4)|
|---|---|---|---|---|
|Age|<br>0.00|<br>0.00|<br>0.00|<br>0.00|
||0.00|0.00|0.00|0.00|
|Primary|0.19|0.22|0.18|0.20|
||(0.09)|(0.09)|(0.10)|(0.10)|
|Secondary|0.19*|0.23*|0.18|0.21*|
||(0.09)|(0.09)|(0.10)|(0.10)|
|College|0.25|0.26|0.19|0.22|
||(0.19)|(0.19)|(0.22)|(0.22)|
|TVET|0.19***|0.22**|0.19***|0.21***|
||(0.05)|(0.05)|(0.06)|(0.06)|
|Sex|0.00|0.00|0.01|0.00|
||(0.05)|(0.06)|(0.06)|(0.06)|
|Apprenticeship<br>|0.17|0.15|0.16|0.15|
|completed|||||
||(0.04)|(0.04)|(0.05)|(0.05)|
|**_Activity Prior to Curre_**|**_nt Job_**||||
|-unemployed||0.15||0.14|
|||(0.12)||(0.12)|
|-employee||0.12||0.14|
|||(0.11)||(0.12)|
|-self-employed||0.03||0.13|
|||(0.14)||(0.17)|
|-casual||0.21||0.27|
|||(0.11)||(0.11)|
|-student||-0.03||0.07|
|||(0.12)||(0.12)|
|**_Employment History_**|||||
|-unemployment|||0.21***|0.20***|
||||(0.04)|(0.05)|
|-government|||0.13|0.10|
||||(0.06)|(0.07)|
|-employee|||-0.01|-0.05|
||||(0.04)|(0.06)|
|-domestic|||0.20|0.17*|
||||(0.07)|(0.08)|
|-family worker|||0.10|0.11|
||||(0.13)|(0.13)|
|-self-employed|||-0.09|-0.08|
||||(0.09)|(0.12)|
|-cooperative|||0.32***|0.31***|
||||(0.08)|(0.08)|
|-casual|||-0.13|-0.17|
||||(0.07)|(0.07)|
|N|661|658|<br>661|<br>658|
|LR Chi2|101.6(17)|110.44(23)|153.69(25)|161.12(25)|
|Pseudo R2|0.1109|0.1282|0.1686|0.1774|

Note:

-*, **, *** indicate significance at the 10%, 5% and 1% level respectively

-Marginal effects, evaluated at the mean

-17-
The second reason is that, despite its decline in relative size, agriculture continues to play a crucial role in ensuring a successful structural transformation of the economy in the long run. In nonindustrialized countries agriculture is the only source of government revenue. A sharp and sustained increase in agricultural productivity is a precondition for industrial development. All of today’s high-income countries kick started industrialization by taxing their reformed agricultural sectors.<sup>‡</sup> In the subsequent early industrialization phases, a productive agricultural sector becomes a key contributor to overall growth by stimulating demand for industrial products, containing urban wages by producing affordable food and ensuring foreign currency inflow through agricultural exports. As agriculture integrates with the rest of the economy, capital and labor progressively shift towards uses with higher returns in manufacturing and services. This triggers a convergence in the labor productivities – and therefore incomes – of the two sectors, but important gaps persist to the present in rich countries (Table 2).

**Table 2.** Average labor productivity by income group, 2000-15 (thousand US$ per worker)

||**Agriculture**|**Manufacturing**<br>**and Services**|**gap**|
|---|---|---|---|
|High income|39.0|74.5|47.7%|
|Upper middle income|6.9|18.8|63.3%|
|Lower middle income|2.5|6.4|61.5%|
|Lowincome|0.5|3.5|85.9%|
|**Total**|**21.5**|**43.1**|**50.1%**|

Source: Authors’ calculations based on World Development Indicators.

Notes: Labor productivity is calculated as the ratio between the GDP and the number of workers in each sector. The labor productivity gap is calculated as the labor productivity in agriculture expressed as a share of that in manufacturing and services.

Significant imbalances between urban and rural incomes generate political tensions in response to which governments tend to protect the agricultural sector from international competition and ultimately provide direct income subsidies to farmers (Lindert, 1991). Even for industrialized countries – most of which severely subsidize their farmers – raising agricultural productivity is key to contain the distortionary and budgetary costs of agricultural protection (Timmer, 1988).

Despite what is discussed above, much of the economic literature has treated agriculture as a marginal sector, leaving many questions around the drivers of agricultural development

> ‡ Small states with no or minimal agriculture, such as Singapore or Hong Kong SAR, China, represent clear exceptions.

4