rafmacalaba/datause-encoder-data huggingface dataset:
of spatial econometrics to a research question that has traditionally been investigated datasets. We instead use a framework that allows for more using cross-country panel accurate of the fact that both climatic shocks and urbanization are modeling potentially correlated over time and across space.

The structure of is in our a spatial dependence approach approximated by symmetric row-standardized weighting matrix _W_ . The spatial lag of each variable can then be derived by multiplying the matrix by a vector of observations. Our baseline approach relies on calculating Queen contiguity-based spatial weights. The Queen approach implies that spatial dependencies for each cell are calculated based on the eight neighboring cells that surround the destination cell. Given that each cell corresponds to roughly 55 _×_ 55 km, this approach captures spatial dependencies within square-shaped areas whose sides are 165 km in length each. Spatially lagged drought is then calculated by taking the average value of drought from across the neighboring cells. As part of our robustness checks, we vary the assumed structure of spatial dependence by introducing alternative weighting matrices.

# **3.2 Measuring Urban Extent**

To measure the extent of land within each cell in a we use built-up grid given year, remotely sensed data from the World Settlement Footprint (WSF) (Marconcini _et al._ , 2020). Employing multitemporal optical satellite imagery, the WSF provides a global 30m resolution of human settlements. Using this novel resource, we first calculate the total number of built-up pixels that a given grid cell contained in 1985 and we use this variable as a baseline control of urban land present in each grid cell. As our dependent variable, we calculate the increment, i.e. the total number of new built-up pixels that were built in a given cell in a given year. Figure 1 below illustrates this methodology by displaying the trajectory of urban growth in Uganda’s capital city, Kampala. The figure displays five-year increments in built-up pixels, showing how Kampala’s urban extent gradually expanded within the specific grid cell where it is located.<sup>6</sup> We first calculated the baseline

> 6Note that our regression analyses use data that ends in 2014 due to the extent of availability of the drought measure that we use. Figures where we only display data based on the World Settlement Footprint database allow us to go beyond 2014.

8
Policy Research Working Paper 5072

# **Abstract**

Remittances are a sizeable source of external financing for developing countries. In the L’Aquila 2009 G8 Summit, leaders pledged to reduce the cost of remittances by half in 5 years (from 10 to 5 percent). Yet, empirically, little is known about what drives the cost of remittances. Using newly gathered data across 119 country corridors, this paper explores the factors that determine the cost of remittances. Considering average costs across all types of institutions, the authors find that corridors with larger numbers of migrants and more competition among remittances service providers exhibit lower costs. By contrast, remittance costs are higher in richer corridors and in corridors with greater bank participation in the remittances market. Comparing results across all banks and all money transfer operators separately, the analysis finds few significant differences. However, estimations for Western Union, a leading player in the remittances business, suggest that this firm’s prices are insensitive to competition.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the factors that drive the cost of remittances across corridors. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted atmmartinezperia@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
**Calcium, Niacin, Riboflavin, Thiamin, and Folate** : Table 6 presents the results for this last set of nutrients. Based on fixed effect estimates, calcium adequacy ratio for boys are 15% higher than that for girls. The corresponding ratio for male adolescents are 11% higher than that for female adolescents, and adult females’ calcium adequacy ratio are 32% lower than that of adult males. Compared with adult males, the situation for pregnant (83% lower) and lactating females (74% lower) are even worse. While female adults’ niacin adequacy ratio are not significantly different from that of male adults, both pregnant and lactating women have 19% and 14% lower niacin adequacy ratio than that of adult males. Sex disparity among children is also significant with boys having higher niacin adequacy ration than girls. While pregnant women’s riboflavin adequacy ratio are not significantly different from that of adult males, lactating women’s adequacy ratio are 8% lower and female adults’ ratio 13% lower than that of adult males. Boys’ riboflavin adequacy ratio are 9% higher than girls’. Similar pattern of sex disparity across all age-sexphysical status groups is observed for thiamin and folate as well. For folate, while girls’ adequacy ratio is not significantly different from that of adult males, boys’ adequacy ratio are about 10% higher than that of adult males (and girls).

Evidence of significant intrahousehold bargaining appears (bottom panel of Table 6) for calcium allocation for male and female adolescents and female adults and riboflavin and thiamin allocations for male adolescents. A wife’s assets as opposed to head’s assets, positively affect each of these allocations. For instance, doubling a wife’s assets would increase the calcium adequacy ratio for adult females by 4%, while the effect of corresponding increase in head’s assets is not significantly different from zero.

Allocation of these nutrients also imply that households tend to compensate for low individual health endowments. Although inelastic, intakes of niacin and thiamin are positively associated with the increase in household expenditure at lower level of expenditure. OLS estimates also indicate significant economies of scale for individual consumption of these nutrients within the household. There does not appear to be a consistent pattern of seasonality, perhaps because seasonality varies for different foods. Compared to round 4, calcium intake is about 6% higher in round 1, niacin intake is about 7% higher in round 2 and 4% higher in round 3, riboflavin intake is 6.5% lower in round 3, folate intake is about 13% lower in round 3, and thiamin intake is about 9% higher in round 3.

25
Policy Research Working Paper 9376

# **Abstract**

Smallholder farmers’ investment decisions and the efficiency of resource allocation depend on the security of land tenure. This paper develops a simple model that captures essential institutional features of rural land markets in Ghana, including the dependence of future rights over land on current cultivation and land rental decisions. The model predictions guide the evaluation of a pilot land titling intervention that took place in an urbanizing area located in the Central Region of Ghana. The evaluation is based on a regression discontinuity design combined with three rounds of household survey data collected over a period of six years. The analysis finds strong markers for the program’s success in registering land in the targeted program area. However, land registration does not translate into agricultural investments or increased credit taking. Instead, treated households decrease their amount of agricultural labor, accompanied by only a small reduction of agricultural production and no changes in productivity. In line with this result, households decrease their landholdings amid a surge in land valuations. The analysis uncovers important within-household differences in how women and men respond differentially to the program. There appears to be a general shift to nonfarm economic activities, and women’s business profits increased considerably.

This paper is a product of the Gender Innovation Lab, Africa 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 nbuehren@worldbank.org and mgoldstein@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
14 1960-65, to 116 in 1965-70, to 104 in 1970-75,<sup>to 88 in 1975-80, and end up to</sup> a low average of 71 for the 1980s.

Within Middle East and North Africa,<sup>the differences between countries</sup> are widespread. Kuwait has a 5qO<sup>of only 27 per thousand in 1980-85, compared</sup> with 141 for Turkey and 166 for<sup>Egypt.</sup> This supports the conclusion of Hill<sup>and Pebley, that "child mortality</sup> is, and has been over the entire period<sup>under consideration,</sup> higher in Africa than in [...] other regions. Second, even within<sup>regions and time periods</sup> there is a tremendous range of child mortality<sup>conditions. Third, in all</sup> [...I regions, child mortality has generally<sup>been falling over the last two</sup> and a half decades."<sup>8</sup> Inter- and extrapolated UN data (Table<sup>Health 2) give a similar story,</sup> yet a more complete one. Africa as<sup>a whole remains above 200 per thousand for</sup> the 1980-85 quintile, a decidedly<sup>poor performance. Only middle income</sup> African countries pass below the threshold<sup>of 200 with a weighted average of</sup> 180 per thousand in 1980-85. Countries<sup>with the biggest under five mortality</sup> are the Gambia (302), Mali (312),<sup>and Sierra Leone (312). Mauritius brings</sup> the best success story with only 36<sup>per thousand, the only country in Africa</sup> to fall below 100. It did, however,<sup>start at only 89 in 1960-85.</sup> Here too South Asia starts at<sup>a similar point to Africa (259 in 196-65)</sup> but improves its under five mortality<sup>further, to 169 per thousand in 1980-85.</sup> 8Hill and Pebley (1989), op.cit. p.6.
29

# **Figure A1**

## **Simulated Regional Poverty Rates for Selected Regions**

<!-- Start of picture text --> 0.80<br>0.70<br>0.60<br>0.50 Lindi<br>Mwanza<br>Mara<br>0.40 Mtwara<br>Rukwa<br>Morogoro<br>0.30 Mbeya<br>0.20<br>0.10<br>0.00<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Year<br>Headcount Poverty Rate<br><!-- End of picture text -->
values for the determinants of debt, I use the dynamic equations (3.1)(3.2) evaluated at the residuals _ζt_ .

Figure 7 reports the baseline projection of foreign debt (solid line), stress tests ignoring co-movements (dashed line), and stress tests taking into account the correlation between the reduced form shocks and feedback eects between the determinants of debt (circled line). Each panel represents a stress test equal to one standard deviation shock to the proposed determinant of debt lasting for two years. The dierence between the dashed and circled lines represents the bias induced by ignoring co-movements. In most cases, this dierence is non-trivial and, typically, ignoring co-movements underestimates the increase in foreign debt. For example, while a negative shock to FDI barely changes external debt according to the IMFWorld Bank stress test, it does induce a sizable increase in foreign debt once the co-movements are taken into account. In sum, this exercise suggests that ignoring comovements, as currently done in the IMFWorld Bank debt sustainability framework, could lead to important biases in the projected trajectories of external debt.

# 5 Robustness checks

This section discusses three alternative specications to assess the robustness of the baseline estimation. First, I consider the stability of the results to the sample of countries included in the estimation. Second, I discuss results for the model chosen by the AIC criterion, which includes two lags in the endogenous variables and two lags in the exogenous variables. Finally, I re-estimate the model without exogenous variables. The robustness of the results is assessed by comparing predicted debt trajectories and probabilities that debt crosses the proposed bounds in the example economy under the baseline and alternative specications.

Consider rst the stability of the results to the sample of countries used in the estimation. The top panel of Figure 8 displays results for a sub-sample of countries with 15 years of data or more (the sample is reduced from 72 to 49 countries). The remaining panels of the gure 24
trade, transport, communications and construction—employed approximately 9 percent of the labor force, split fairly evenly between urban and rural areas, accounting for approximately onethird of GDP. Government accounted for the largest share of GDP, at approximately 20 percent, and 7 percent of the labor force. Most of the public labor force is situated in rural areas where the population lives, but approximately one-third is based in urban areas, especially the public administration hub of Kampala.

|<br>**Ugand**|<br>**TABLE**<br>**a GDP Sector Shar**|<br>**S4.1**<br>**es, 1990/91 - 20**|<br>**04/05**||
|---|---|---|---|---|
|MONETARY SECTORS|1990/91|1994/95|1999/00|2004/05|
|Agriculture|22.7%|25.2%|20.3%|18.7%|
|- Cash crops<br>|2.8%<br>|6.3%<br>|3.6%<br>|2.8%<br>|
|- Food crops|12.3%|12.2%|10.7%|10.2%|
|- Livestock|4.1%|3.4%|3.4%|2.7%|
|- Forestry|1.1%|0.9%|0.7%|0.7%|
|- Fishing|2.4%|2.3%|2.0%|2.3%|
|Mining & quarrying|0.3%|0.3%|0.8%|0.9%|
|Manufacturing|5.7%|7.2%|9.4%|9.0%|
|Electricity/water|0.9%|1.3%|1.4%|1.3%|
|Construction|4.8%|5.5%|7.6%|9.3%|
|Wholesale & Retail Trade|12.3%|12.0%|11.2%|10.9%|
|Hotels & Restaurants|1.3%|1.8%|2.4%|3.3%|
|Transport/communication|4.0%|3.9%|5.3%|7.7%|
|- Road|2.9%|3.0%|3.7%|3.5%|
|- Rail|0.3%|0.2%|0.2%|0.1%|
|- Air & Support. Services|0.4%|0.4%|0.5%|0.5%|
|- Communications|0.4%|0.4%|0.9%|3.6%|
|Community services|15.4%|17.0%|20.4%|20.0%|
|- General government|3.2%|4.5%|4.3%|4.2%|
|- Education|3.9%|4.0%|6.1%|6.4%|
|- Health|1.5%|1.3%|2.2%|2.3%|
|- Rents|3.2%|3.5%|4.3%|3.8%|
|- Miscellaneous|3.6%|3.8%|3.6%|3.2%|
|TOTAL MONETARY|67.4%|74.1%|78.7%|81.0%|
|NON-MONETARY SECTORS|||||
|Agriculture|28.4%|22.3%|16.9%|14.8%|
|- Food crops|25.7%|19.9%|13.7%|11.9%|
|- Livestock|1.3%|1.2%|1.9%|1.7%|
|- Forestry|1.1%|0.9%|1.2%|1.0%|
|- Fishing|0.3%|0.3%|0.3%|0.3%|
|Construction|0.8%|0.7%|0.6%|0.5%|
|Owner-occupied Dwelling|3.3%|2.9%|3.7%|3.7%|
|TOTAL NON-MONETARY|32.6%|25.9%|21.3%|19.0%|

Sources: Uganda Ministry of Finance, Planning and Economic Development 2005; authors' calculations.

61
29 1) Figures 12-15 report the plots of the risk premium for the South American countries and Nigeria on the period of the Mexican crisis (7/29/94 to 7/28/95)31: the plots show that all five countries had a significant response to the crisis. Moreover, NIGPAR is affected by the peaks of MEXPAR but it follows a much more volatile path, and it has a behavior very similar to VENPAR.

<!-- Start of picture text --> 3000 200D__ ____ _______________ ____ _______<br>2500<br>2000<br>1000 ----<br>500<br>0-<br>7/299 10/0194 12116194 2/24/95 5/0195 7114195 7/2Q'94 10/07/94 12/i694 2/24/95 -605/96 7114196<br>NIGPAR ------- MEXPAR - VENPAR MEXDIS- ARGDI<br>Figure 12 Figure 13<br>2000 2500<br>1600 - 2000<br>1200 - 1500<br>800 -- 1000<br>400- 500<br>7/12St94 '10/0Y94 l'f1Vi94 '2i2/495 "6 i0&/9'5 i4/5 7/29194 -10/0i94 '12/1694 'V24195 'J05 ' 7i M4/95<br>- MEXDIS ----- BRADIS - MEXDIS -- ECUDIS<br>Figure 14 Figure 15<br><!-- End of picture text --> Consider now the other countries in our sample, Philippines and Bulgaria, in figure 16-18 . Note how similar to Poland's is the behavior of the Philippines' bond: the difference between MEXPAR and PHIPAR (DIFF) shows the _31_ A first glance at the plots in figure 5 shows that there is no strong evidence of comovements in the risk premium except during periods in which one of the countries experiences a relevant crisis. We have divided the countries in _groups_ on the basis of different factors to see if there was any evidence of comovements in the sub-groups. First, the _oil producers:_ Mexico, Venezuela and Nigeria; while Venezuela and Nigeria move together during the Mexican crisis (7/29/94 to 7/28/95), on the whole sample the three scries have a very different behavior. Second, the _South American countries:_ Mexico, Brazil, Argentina, Ecuador and Venezuela; again, during the Mexican crisis the comovements are evident, but this is not the case for the whole sample. The same conclusion holds if we divide the countries on the basis of the _date in which the Brady deal was announced_ or actually _implemented._
<u>Table 4: Cyclical Properties of Transition Rates among Employment Sectors: Mexico and Brazil</u>

|||Mexico<br>||||Brazil<br>|||
|---|---|---|---|---|---|---|---|---|
|Series|St. Dev|Correlation|Elasticity|Cross Cor|St. Dev|Correlation|Elasticity|Cross Cor|
|S-I|0.030|-0.013|-0.014|0.424|0.037|0.008|0.014|0.621|
|I-S|0.035|0.092|0.122||0.033|-0.187|-0.314||
|I-F|0.038|0.574|0.798|0.174|0.039|0.740|1.467|0.486|
|F-I|0.026|-0.273|-0.269||0.041|0.319|0.658||
|S-F|0.074|0.425|1.171|0.670|0.061|0.303|0.946|0.761|
|F-S|0.069|0.179|0.416||0.053|0.353|0.951||

Notes: The table shows the standard deviation, the correlation with output and the elasticity with respect to oput of the logged and HP filter de-trended transition rates across employmetn states (S,I and F) for Mexico and Brazil. We also report the cross correlation among bi-lateral flows. Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

43
representative. For some other categories, there are domestic substitutions for imports, Hee, there are two kinds of enors involved in the companison in question. First the selection of imported and luxury goods does not meet the requirement of charcteisticity, because it is sure that these goods account for a small share of the goods actually consumed in these categories in China Second, the price differentials between imported and domestic goods only partially reflec **t** he quality differentials. In other words, the imported goods are more expensive not just because their quality is higher than that of similar domestic products, but also because they are ovepriced just because they are imported goods. Generally spealdng the inclusion of the prices of imported and luxury goods in the sample of prices would lead to an upwards bias in estimating real income in the first case and an downwards bias in the second ca. The former soue of errors should be considered seriously because the quality matching is always the key issue in the ICP-type comparison between a developing and a developed country. In order to explore how the quality diffeential afts the esimate of PPPs in these categories, an adjustnent of 50%/O has been made in Clothing (02.100) and Furniture (04.100). 69. An assumption of no intemational productivity diffential between professional personnel was nade for the comparison hi those comparison-resistanca egories. Many sudies under ICOP project **t** have demonstrated that the productivity gaps between the countries can be very differen among the industries (van Ark, 1993, van _;._ rk and Pilat, 1993), and the other studies have shown that the service sectors in the low-income countnes could have relative hier productivty h the ma g sectors (Bhagwati 1984). There are a lot of unsolved questions involved in the measu t of service sectors in the national accounts and in the intnational compaisons (see Kravis et al. 1982, pp.129-162).

30
**Table 1:** This table shows the livestock type with the conversion factor. Sources: Eurostat (2018)

|**livestock type**|**conversion factor**|
|---|---|
|Cattle|1|
|Pigs|0.3|
|Goats|0.1|
|Sheep|0.1|
|Chicken|0.01|

of the animal equivalent values are multiplied by pixel areas to get the count of animals per grid, which is multiplied by the FAOSTAT’s value of production to obtain the livestock production prior for each pixel.

where _lsvali_ is the total value of livestock production in pixel i; _lsvalx_ is the value of livestock production (meat, milk, eggs, honey and wool) that is reported at the national level; _lsnumi_ is the total number of equivalent animals in pixel _i_ ; and _X_ is a set including all pixels that fall within the boundary of a nation.

A map of global gridded livestock production value as a prior is shown in Figure 2.

# **2.1.3 Forestry production and hunting**

Forest resources have been utilized by people since the advent of civilization (Hossain, Alam, Miah, et al., 2008). Up until now, over a billion people still rely on forest resources for food security and income generation to some extent (FAO, 2018). In the world’s least developed regions, 34 countries depend on fuelwood to provide more than 70% of energy, among which 13 nations require 90% of energy (FAO, 2018).

The contribution of forest production to AgGDP can be classified into two broad types: wood (logging) products and non-wood forest products. Wood (logging) products are the most exploited commodities in the forestry sector. The trees are cut down to be the raw materials for producing timber and pulp, which are further processed and converted into a number of derivatives, such as construction materials and paper products. Non-wood forest products are defined by the Food and Agriculture Organization of the United Nations (FAO).<sup>4</sup> It is estimated that millions of households around the world depend on non-wood

> 4These products are “goods of biological origin other than wood derived from forests, other wooded land and trees outside forests”, including foods (nuts, fruits, mushrooms, etc.), food additives (herbs, spices, sweeteners, etc.), fibers (for construction, furniture, clothing, etc.), and plant and animal products with chemical, medical, cosmetic or cultural value.

8