rafmacalaba/datause-encoder-data huggingface dataset:
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|---|---|
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|National Statistics and Data for Development|Carlos Chadreque Penicela Da Maia|
|Shaanxi Sustainable Towns Development Project|GuangmingYan|
|Shaanxi Sustainable Towns Development Project|GuangmingYan|
|Shaanxi Sustainable Towns Development Project|GuangmingYan|
|EnhancingResilience in Kyrgyzstan Project|Ko Takeuchi|
|EnhancingResilience in Kyrgyzstan Project|Ko Takeuchi|
|EnhancingResilience in Kyrgyzstan Project|Ko Takeuchi|
|EnhancingResilience in Kyrgyzstan Project|Ko Takeuchi|
|Rural Water Supplyand Sanitation Project|Farzona Mukhitdinova|
|Rural Water Supplyand Sanitation Project|Farzona Mukhitdinova|
|Rural Water Supplyand Sanitation Project|Farzona Mukhitdinova|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|StrengtheningSocial Protection Project|Iftikhar Malik|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Smallholder Agricultural Production Restoration and Enhancement|Rufiz Vakhid Chirag-Zade|
|Empoweringfarmers at district level through social accountability|toWinston Dawes|
|Empoweringfarmers at district level through social accountability|toWinston Dawes|
|Rwanda EnergySector Development PolicyLoan|Yadviga Viktorivna Semikolenova|
|Rwanda EnergySector Development PolicyLoan|Yadviga Viktorivna Semikolenova|
|Cambodia Nutrition Project|Ziauddin Hyder|
|Cambodia Nutrition Project|Ziauddin Hyder|
|West Bengal Major Irrigation and Flood Management Project|IJsbrand Harko de Jong|
|Gas Storage Expansion Project|Manuel Berlengiero|
|Gas Storage Expansion Project|Manuel Berlengiero|
|Gas Storage Expansion Project|Manuel Berlengiero|
|Gas Storage Expansion Project|Manuel Berlengiero|
|Solomon Islands MiningGovernance|Bryan Christopher Land|
|Solomon Islands MiningGovernance|Bryan Christopher Land|
|Solomon Islands MiningGovernance|Bryan Christopher Land|
|Djibouti Household Survey2017|Gabriel Lara Ibarra|
|ElectricityService Access Project|Christopher Saunders|
|ElectricityService Access Project|Christopher Saunders|
|ElectricityService Access Project|Christopher Saunders|
155–68.

Fiess, N., and D. Verner. 2003. “Migration and Human Capital in Brazil during the 1990s.” Policy Research Working Paper 3093, World Bank, Policy Research Department, Washington, DC.

Fujita, M., P. Krugman, and A. Venables. 1999. _The Spatial Economy: Cities, Regions, and International Trade_ . Cambridge: MIT Press.

Grogger, J., and G. H. Hanson. 2011. “Income Maximization and the Selection and Sorting of International Migrants.” _Journal of Development Economics_ 95 (1): 42–57.

Hanson, G. 2005. “Market Potential, Increasing Returns, and Geographic Concentration.” _Journal of International Economics_ 67 (1): 1–24.

Head, K., and T. Mayer. 2006. “Regional Wage and Employment Responses to Market Potential in the EU.” _Regional Science and Urban Economics_ 36 (5): 573– 94.

———. 2011. “Gravity, Market Potential and Economic Development.” _Journal of Economic Geography_ 11 (2): 281–94.

———. 2013. “Gravity Equations: Workhorse, Toolkit, and Cookbook.” CEPR Discussion Papers 9322, C.E.P.R. Discussion Papers.

Helpman, E., O. Itskhoki, and S. Redding. 2010. “Inequality and Unemployment in a Global Economy.” _Econometrica_ 78 (4): 1239–83.

Henley, A., G. R. Arabsheibani, and F. G. Carneiro. 2009. “On Defining and Measuring the Informal Sector: Evidence from Brazil.” _World Development_ 37 (5): 992–1003.

Hering, L., and S. Poncet. 2010. “Market Access Impact on Individual Wages: Evidence from China.” _Review of Economics and Statistics._ 92 (1): 145–59. Kovak, B. 2011. “Local Labor Market Effects of Trade Policy: Evidence from Brazilian Liberalization.” mimeo.

———. 2013. “Regional efects of trade reform: What is the correct measure of liberalization?” _American Economic Review_ 103 (5): 1960–76.

Letouzé, E., M. Purser, F. Rodríguez, and M. Cummins. 2009. “Revisiting the 33
**Table 29. Reactions to political risks: Most significant political risk**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.0987|-0.0790|||-0.138|-0.114|||
||(0.0660)|(0.0665)|||(0.109)|(0.115)|||
|Export % of sales|-0.000662|0.000246|-0.000865|3.13e-05|-0.000594|0.000490|-0.000856|0.000282|
||(0.000864)|(0.000894)|(0.000899)|(0.000921)|(0.00141)|(0.00155)|(0.00149)|(0.00162)|
|Share of sourcing via imports|0.000395|0.00146|0.000837|0.00186*|0.000466|0.00221|0.00112|0.00303*|
||(0.000923)|(0.000941)|(0.000966)|(0.000992)|(0.00154)|(0.00165)|(0.00167)|(0.00177)|
|High-income source country|-0.00739|0.00110|0.0202|0.0186|0.00294|-0.0204|0.0432|0.0133|
||(0.0912)|(0.0955)|(0.0924)|(0.0949)|(0.152)|(0.166)|(0.159)|(0.168)|
|>250 employees|0.0471|0.0205|0.0498|0.0118|0.0408|0.0166|0.0392|-0.00626|
||(0.0814)|(0.0799)|(0.0825)|(0.0804)|(0.136)|(0.138)|(0.141)|(0.142)|
|Cumulative inv. >US$10M|0.0373|0.0251|0.0692|0.0488|0.106|0.0717|0.158|0.112|
||(0.0810)|(0.0818)|(0.0839)|(0.0840)|(0.136)|(0.141)|(0.143)|(0.147)|
|Years in country|-0.00504**|-0.00518**|-0.00505**|-0.00501**|-0.00779**|-0.00739**|-0.00778**|-0.00717*|
||(0.00244)|(0.00236)|(0.00246)|(0.00241)|(0.00372)|(0.00372)|(0.00387)|(0.00389)|
|Level of foreign ownership (%)|0.000802|0.000762|0.000765|0.000689|0.00230|0.00210|0.00226|0.00204|
||(0.00130)|(0.00136)|(0.00135)|(0.00137)|(0.00212)|(0.00233)|(0.00224)|(0.00238)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.026|0.085|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is a composite ordered categorical variable constructed from responses to the following series of questions: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation." Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

46
(in 2016 dollars). Focusing on household consumption alone, the estimates suggest a burden of 1.1 percent of expenditure, or $203 per citizen per year. Applying the same approach to investment spending generates an estimate that the JA increases the cost of investment in PR by 3 percent.

Our estimates of missing trade among upstream products are more difficult to attribute directly to the JA, but are potentially much more important since they suggest dynamic effects of the JA. We find that PR’s total imports of sea-shipped upstream products are approximately 77% lower than imports of otherwise equivalent air-shipped products. Similar calculations for DOM, JAM and BHS finds no bias against sea-shipped products in upstream import demand. Although the bias against sea-shipped upstream products in PR’s import demand is suspiciously large, attributing these effects to the JA is difficult because the evidence of additional bias against U.S. mainland sources is weak. We therefore refrain from including the effects we observe among upstream products in our welfare estimates. If even a fraction of the effects we estimate are in fact due to the JA, the policy has imposed a large burden on PR’s long-run development.

There is a relatively small academic literature on the JA. The paper closest to ours is Olney (2020), who shows evidence of substitution away from waterborne shipping among data on shipments arriving in U.S. coastal states. Our econometric exercise is similar, but with a few key differences. We focus on PR (an island) rather than coastal U.S. states, and use data on Puerto Rican imports rather than freight movements destined for U.S. mainland ports. Our focus on PR’s trade means that only two modes of transport are relevant (air and sea), while Olney’s data contain possibilities for easier substitution towards rail and road transport. We estimate a structural parameter, the tariff-equivalent cost of JA restrictions that can explain cross-product variation in estimated home bias that is attributable to the relevant product characteristics. These estimates reveal relatively higher tariff equivalents 5
in 2013, more than triple the amount in 2001. A few non-oil sectors including apparel, footwear and agricultural produce experienced increases in US imports from AGOA countries during this period. In order of importance in non-oil exports, transportation equipment, minerals and metals, textile and apparel, agricultural products and chemicals and related products accounted for the biggest shares.

Table A4 in the appendix presents average annual exports to the US (in constant 2000 USD) for the countries included in the study for four periods between 1993 and 2015. Figure 1 presents the average annual exports of AGOA beneficiaries before and after AGOA for the entire period 1993-2015, where the particular year of eligibility varies across countries. Among the major exporters, Nigeria, Angola, South Africa, the Republic of Congo and Chad have registered significant increases in exports to the US post- AGOA. Other countries that increased exports include Kenya, Lesotho, Ghana, Cameroon, Botswana, Namibia, Ethiopia and Liberia. The next section discusses if the rise in exports is associated with AGOA, by presenting the estimated impact using SCM.

19
**Table 8. Maturity composition of debt and estimated short-term effects of a 1 percentage point increase in euro-area interest rates.**

|**Country**|**Short term debt as**<br>**a percentage of**<br>**total foreign debt**|**Variable-rate long-**<br>**term debt as a**<br>**percentage of total**<br>**foreign debt**|**Total external debt**<br>**as a percentage of**<br>**exports of goods**<br>**and services**|**Increase in 1999**<br>**debt service to**<br>**exports ratio,**<br>**(percentage**<br>**points)**|
|---|---|---|---|---|
|**_West and Central Africa_**|||||
|Benin|8.9|0.9|268.9|0.047|
|Burkina Faso|5.5|0.0|295.5|0.016|
|Carneroon|14.3|16.1|368.7|0.618|
|Central African Republic|7.3|0.6|410.1|0.038|
|<br>Chad|2.7|0.1|364.8|0.013|
|Congo, Rep. of|_15.0_|20.8|280.9|0.551|
|<br>Cote d'lvoire|17.6|43.5|314.6|0.686|
|Equatorial Guinea|22.7|1.5|65.7|0.043|
|<br>Gabon|11.6|13.5|129.6|0.206|
|Guinea-Bissau|7.8|1.7|1,645.2|0.450|
|Mali|3.0|0.1|395.4|0.043|
|Niger|6.0|7.5|509.4|0.290|
|<br>Senegal|6.3|7.0|226.6|0.090|
|Togo|3.5|10.4|198.0|0.074|
|<br>Gambia, The|3.1|0.0|184.8|0.007|
|Ghana|12.0|5.2|349.4|0.062|
|Guinea|12.1|0.1|469.7|0.135|
|Liberia|37.8|7.2|||
|Mauritania|13.0|5.6|553.8|0.316|
|Nigeria|19.4|17.1|156.6|0.155|
|Sierra Leone|9.0|1.1|1,235.2|0.208|
|**_East and Southern_**|||||
|<br>**_Africa_**|||||
|Angola|12.6|6.4|191.4|0.026|
|Botswana|7.1|12.0|||
|Burundi|1.5|0.0|1,062.3|0.018|
|Congo, Dem. R.|27.7|11.1|845.7|1.112|
|<br>Eritrea|0.0|0.0|19.1|0.000|
|Ethiopia|5.7|0.2|962.9|0.186|
|<br>Kenya|12.9|9.9|214.9|0.087|
|Lesotho|1.2|9.2|93.5|0.027|
|Madagascar|4.1|5.9|522.2|0.125|
|Malawi|1.3|0.3|351.3|0.001|
|Mauritius|20.1|53.9|95.4|0.176|
|Mozambique|5.6|8.4|1,066.0|0.270|
|Namibia|..||||
|Rwanda|7.2|0.0|673.3|0.100|
|Somalia|23.1|0.8|||
|South Africa|441|242|678|0012|
|<br>Sudan|.<br>38.9|.<br>11.47|.<br>1,459.8|.<br>2.257|
|Tanzania|12.1|4.9|<br>576.5|0.129|
|Uganda|3.4|2.3|429.6|0.015|
|<br>Zambia|6.7|7.2|501.3|0.158|
|Zimbabwe|21.4|21.0|159.2|0.180|
|**_Sub-Saharan_**_Africa_|**19.3**|**0.14**|**201.7**|0.173|

_Note:_ A number of assumptions underlie the calculations: (1) Euro-area interest rates increase by I percent in 1999; (2) there are no changes in domestic interest rates, exchange rates, debt stocks, or exports as a result of the change in euroarea interest rates; (3) the currency composition of short-term and variable-rate long-term debt is the same as that of total long-term debt; (4) the interest rate of the Swiss franc follows that of the euro; (5) 50 percent of the "other" currencies are from the euro-area.

_Source:_ World Bank 1999c and authors' calculations.

20
_Table 4: Total differential decomposition of change in inequality, 1993-98_

|'|a's<br>|Means of x's<br>|Cis|_GC,_|Total|
|---|---|---|---|---|---|
|Childs age (in months)<br>'|0.003<br>|0.011<br>|-0.002||0.012|
|Childs age squared<br>|_0.003_<br>|-0.010<br>|0.001||-0.006|
|Child - male<br>|0.001<br>|0.000<br>|0.000||0.001|
|Household consumption<br>|-0.005<br>|-0.005<br>|-0.002||-0.011|
|Safe drinking water<br>|-0.002<br>|0.000<br>|0.000||-0.003|
|Satisfactory sanitation<br>|0.003<br>|-0.002<br>|0.000||0.001|
|Years schooling household head<br>|0.001<br>|0.000<br>|-0.001||0.000|
|Years schooling mother<br>|D.005<br>|0.000<br>|-0A001||0.004|
|Fixed effects<br>""|o.00|-0.014|-0.010||-0a025|
|Residual<br>||||_0.005_|0.005|
|Total<br>Cl % l|0.010<br>|-0.021<br>|-0.016<br>|_0.005_<br>|-0.021|
|oumn as tota|-47%|98%|74%|-25%||

_17_
62

# TABLE 3: Extent of Land Reform by Region, 1985

||Land Distributed<br>(hectares)|Total Area<br>(hectares)|(1)/(2)<br>(X)|
|---|---|---|---|
|All MexIco|102,876,920|196,718,300|52.3|
|North Pacific|18,430,252|41,443,700|44.5|
|North|38,131,949|80,057,800|47.6|
|South Pacific|12,344,546|23,850,000|51.8|
|Centre|13,041,635|27,466,900|47.5|
|Gulf|12,359,717|23,899.900|51.7|

Note: See Table 2 for list of states comprising each region.

Source: INEGI, Estadisticos Hisoricos de Mexico, Tomo I, Mexico D.F., SPP, 1986; Statistical Abstract of Latin America. Vol. 2C. Table 301.
# **8. Conclusions**

The findings in this paper show mixed evidence regarding norm change. Specifically, gender norms that tolerate violence against women become less traditional with displacement, while those that limit women’s economic opportunities become more rigid. These patterns are not necessarily surprising, as the evidence in this area is mixed. For example, Culcasi’s study (2019) of Syrian refugees in Jordan reveals that women can work for pay outside the home, but they continue to be the main caregivers in the household. Similar experiences have also been reported for IDP widows in Nepal, Chechen refugees in the Czech Republic and IDP women in Darfur (De La Puente, 2011; Ramnarain, 2016; Szczepanikova, 2005).

The study also revealed a misalignment between attitudes and behaviors in specific domains of gender norms. For example, displacement is associated with less traditional patriarchal attitudes such as ‘families with men have less problems’ or ‘a good wife obeys her husband,’ but women’s ability to decide about contraception and earnings decreases following displacement. These findings shed light on the complexity of gender norm change, which does not operate in a vacuum. Moreover, change can be contradictory and improvements in one area do not imply that all others will automatically follow.

The findings of this study have implications for policy making. Greater access to reproductive health for displaced women could be ensured through universal health coverage schemes, for example, by including contraception in basic packages. Social assistance programs for IDPs could also address barriers to contraception by providing information to increase uptake and men’s support for modern methods (Khan et al., 2016). While the expansion of reproductive health services might not guarantee a shift in gender norms, it might provide opportunities for women to overcome some of the challenges imposed by those norms (Malhotra et al., 2019).

In terms of economic opportunities, the findings show that, in situations of displacement, paid work does not necessarily translate into increased decision-making power. Hence, providing access to economic opportunities is not a guarantee that gender gaps will be reduced if men have full control of the gains, as determined by patriarchal norms. Economic empowerment programs 29
- Rosenthal, Stuart, and William Strange, “Evidence on the Nature and Sources of Agglomeration Economies,” in _Handbook of Regional and Urban Economics_ , Volume 4, eds. J. Vernon Henderson and Jacques François Thisse (Amsterdam: North-Holland, 2004), 2119–2171.

- Rosenthal, Stuart, and William Strange, “Female Entrepreneurship, Agglomeration, and a New Spatial Mismatch,” _Review of Economics and Statistics_ , forthcoming (2011).

- World Bank, “Planning, Connecting, and Financing Cities—Now,” Urbanization Review Flagship Report, The World Bank, 2013.

26
telecommunications may not be complements but substitutes. In fact, 15 African countries experienced negative growth of fixed-line teledensity over the past five years. This phenomenon may not be characteristics of Africa. From regional data in the Republic of Korea, Sung and Lee (2002) estimate the demand equation for new fixed-line connections, finding that the substitution effect of the rapidly growing mobile network on the conventional ground-based telephone network is significant, with estimated elasticities between -0.179 and -0.097. Rodini _et al_ . (2003) also find the significant fixedmobile substitutability, using a U.S. household survey in 2000-2001. The following empirical model will analyze substitutability between fixed-line, prepaid mobile, and contract-based mobile telecommunications services.

<u>Table 2. Average Teledensity in Sub-Saharan Africa, 1995-2005</u>

||Teledens|ity (per 1,000)|Annual<br>(perc|growth<br>ent)|
|---|---|---|---|---|
||1995|2000<br>2005|1995-2005|2000-05|
|Total subscribers|18.6|53.5<br>219.0|30.0|42.1|
|Fixed-line|17.9|27.5<br>39.9|5.7|3.9|
|Mobile|1.5|29.0<br>145.6|90.7|74.0|
|Prepaid|0.0|13.8<br>124.5|...|77.4|
|Contract|0.7|8.8<br>12.1|...|15.8|

Sources: _International Telecommunication Union_ ; and _EMC_ .

# **_Prices_**

It is difficult to capture the level of telecommunications prices by any single dimension. Indeed, there is no significant correlation between a standard price measured by the unit cost of a local call and penetration rate growth in Africa (Figure 4). This is possibly because the tariff structure of telecommunications services is becoming more complex with a variety of forms of price discrimination. Another reason for the weak relationship may be that price and quantity is jointly dependent on each other. The interdependency has to be solved as a system of structural equations.

10
<!-- Start of picture text --> Agricultural suitabilit Unadjusted mean adjusted mean land<br>g ¥ land value [R$/sqkm] value [R$/sqkm]<br>good, annual & perennial 40,833 52,489<br>regular, annual & perennial 37,974 26,714<br>restricted, annual & perennial 35,838 10,848<br>possible for planted pasture 53,091 49,594<br>possible for natural pasture 36,794 9,216<br>no agricultural value 41 774 5,819<br><!-- End of picture text -->

|Agricultural suitabilit<br>g<br>¥|Unadjusted mean<br>land value [R$/sqkm]|adjusted mean land<br>value [R$/sqkm]|
|---|---|---|
|good, annual & perennial|40,833|52,489|
|regular, annual & perennial|37,974|26,714|
|restricted, annual & perennial|35,838|10,848|
|possible for planted pasture|53,091|49,594|
|possible for natural pasture|36,794|9,216|
|noagriculturalvalue|41774|5,819|

|Biome|Unadjusted mean<br>land value [R¢/sqkm]|adjusted mean land<br>value [R$/sqkm]|
|---|---|---|
|Amazon|35,3829|30,104|
|Transitian|44.112|32,606|
|Cerrada|50,784|40,001|
Figure 3 gives the graph corresponding to Figure 2, but using the survey mean instead of PCE. The pattern is similar to Figure 2, although the least squares elasticity of the poverty line is now even higher at 0.804 (t=15.97).

So the same pattern found by RDV using the older compilations of national poverty lines is evident in Figure 2, with the poverty line rising with mean consumption, but with a low elasticity initially. By interpretation, absolute poverty appears to be the dominant concern in poor countries, with relative poverty emerging at higher consumption levels. However, it is notable how high the overall elasticity is for developing countries.

The nonparametric regression in Figure 2 can be used to test for any regional effects by seeing whether the observations for any region tend to lie systematically above or below the expected value at given consumption, as given by the nonparametric regression line. The most striking regional pattern is that the observations for Asia (both East Asia and South Asia) tended to lie below the regression line; this was true of 11 out of the 12 observations for Asia (all but one of the seven observations for East Asia and all of the five for South Asia had low poverty lines conditional on mean consumption. No other regional patterns emerged so strongly.

The economic gradient in the poverty lines comprises a component due to food needs and one for non-food needs, although we can only quantify this difference for a sub-set of the national poverty lines. For a sub-sample of 28 countries we have complete data for also separating the food from the non-food components of the national poverty lines. The mean food share at the poverty line is 0.564 (with a range from 0.260 to 0.794). We find that the elasticity of the food component of the poverty line to mean consumption is 0.471 (t=9.55), while it is almost twice as high for the non-food component, for which the elasticity is 0.910 (t=8.97). (The overall elasticity for this sub-sample is 0.679, t=11.02, as compared to 0.655 for the full sample.) So the economic gradient in national poverty lines evident in Figure 2 is driven more by the gradient in the non-food component of the poverty lines (which accounts for about 60% of the overall elasticity), although it is notable that there is still an appreciable share attributable to the economic gradient in food poverty lines. By interpretation, the social effect on national poverty lines is determined more by the allowances made for non-food needs than food needs.

> (0.2, 0.9). The mean of the predicted values in the poorest 15 countries ranged from $37.52 to $38.11 (although the regression line was clearly under-smoothed at bandwidths below about 0.5).

11