rafmacalaba/datause-encoder-data huggingface dataset:
Quisumbing, A. R. et al. (2019). _2019 Annual trends and outlook report: Gender equality in rural Africa: From commitments to outcomes: Synopsis._ Washington, DC: International Food Policy Research Institute (IFPRI). URL: https://doi.org/10.2499/9780896293649 <mark>Rexer, J. (2020).</mark> _<mark>The Brides of Boko Haram: Economic Shocks, Marriage Practices, and Insurgency in Nigeria.</mark>_ <mark>The Economic Journal, ueac003. URL:</mark> <u><mark>https://doi.org/10.1093/ej/ueac003</mark></u> Ruiz, I., and Vargas-Silva, C. (2013). _The economics of forced migration._ The Journal of Development Studies, 49(6), 772-784. URL: https://doi.org/10.1080/00220388.2013.777707 <mark>Sarzin, Z. (2017).</mark> _<mark>Stocktaking of Global Forced Displacement Data.</mark>_ <mark>Policy Research working paper, no. WPS 7985; Policy Research Working Paper; No. 7985. World Bank, Washington, DC. © World Bank. URL: https://openknowledge.worldbank.org/handle/10986/26183 License: CC BY 3.0 IGO.</mark> Singh, N. S., et al. (2021). _Research in forced displacement: guidance for a feminist and decolonial_ - _approach._ The Lancet, 397(10274), 560-562. URL: https://doi.org/10.1016/ S0140 <u>6736(21)00024-6</u> Shemyakina, O. (2011). The effect of armed conflict on accumulation of schooling: Results from Tajikistan. Journal of Development Economics, 95(2), 186-200. URL: <u>https://www.sciencedirect.com/science/article/abs/pii/S0304387810000441</u> <mark>- UNHCR (2021). Nigeria Emergency. URL: www.unhcr.org/nigeria</mark> <u><mark>emergency.html</mark></u> UN OCHA (2016). Nigeria - Northeast States: Reference Map. URL: <u>https://www.humanitarianresponse.info/en/operations/nigeria/infographic/nigeria-north-eaststates-28th-april-2016.</u> <mark>UN OCHA (2018). Nigeria: US$1.05B needed to reach 6.1 million people in 2018, 8 February, URL: www.unocha.org/story/nigeria-us105b-needed-reach-61-million-people-2018</mark> UN WOMEN (2020). The World for Women and Girls Annual Report 2019-2020. URL: <u>https://www.unwomen.org/en/digital-library/publications/2020/06/annual-report-2019-2020</u> Vaitla, B., Coates, J., and Maxwell, D. (2015). _Comparing Household Food Consumption Indicators to Inform Acute Food Insecurity Phase Classification._ Washington, DC: FHI 360/Food and Nutrition Technical Assistance III Project (FANTA). URL: https://www.fantaproject.org/sites/default/files/ - - <u>resources/HFCIS report Dec2015.pdf</u> Valentine, G. (2007). Theorizing and researching intersectionality: A challenge for feminist geography. _The Professional Geographer, 59(1),_ 10-21. URL: https://doi.org/10.1111/j.1467- <u>9272.2007.00587.x</u> WFP (2020). World Food Programme — Emergencies. URL: https://www.wfp.org/emergencies WFP (2021). _Emergency Dashboard Nigeria._ URL: <u>https://docs.wfp.org/api/documents/ 333978d1feaf417aa0f2370c77ef8c2d/download/?_ga=2.152636474.220939559.16195934862083797163.1619593486</u> World Bank Group (2016). _North-east Nigeria Recovery and Peace Building Assessment Volume III._ World Bank, Washington, DC. URL: http://hdl.handle.net/10986/25779 21
**Figure A16 – Share of respondents that have previously been vaccinated for another disease, disaggregated by willingness to get the COVID-19 vaccine**

<!-- Start of picture text --> .7<br>.6<br>.5<br>.4<br>.3<br>Planning on being vaccinated<br>Not planning on being vaccinated<br>Unsure about being vaccinated<br>Share of respondents previously vaccinated<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 30 in Appendix B.

**Figure A17 – Whether people encouraged their children to get vaccinated disaggregated by their willingness to get vaccinated**

<!-- Start of picture text --> Have your children been vaccinated?<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Yes 76.3 Yes 80.2Yes 85.2<br>No 11.4 No 10.8 No 5.7<br>know 1.9 Don't know 2.3 Don't know 2.4<br>ildren 10.5 Don't have children 6.7 Don't have children 6.8<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 31 in Appendix B.

38
**Table III – Estimates of the number of treaties on cooperation**

||**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|
|Number of<br>Treaties|2.222*<br>(1.149)|2.218**<br>(1.109)|2.154*<br>(1.136)|2.172**<br>(1.103)|
|CoV<br>|542.363***<br>(169.271)|561.749***<br>(165.968)||570.475***<br>(165.747)|
|(CoV)<sup>2</sup>|-1282.696***<br>(452.247)|-1385.939***<br>(460.032)||-1384.804***<br>(435.890)|
|Ratio Basin<br>(control)|-0.271<br>(0.197)||-0.187<br>(0.192)|-0.227<br>(0.201)|
|Sum Basin<br>(control)|0.023<br>(0.050)||0.0679<br>(0.051)|0.021<br>(0.053)|
|Ratio Basin|-0.004||-0.004|-0.005*|
|(importance)|(0.003)||(0.003)|(0.003)|
|Sum Basin<br>(importance)|-1.862<br>(6.246)||-2.344<br>(6.753)|-0.650<br>(7.077)|
|Alliance||-2.433<br>(3.126)|-3.560<br>(3.557)|-2.203<br>(3.436)|
|Democracy||-1.321***<br>(0.356)|-1.333***<br>(0.374)|-1.334***<br>(0.358)|
|GDP ratio||-0.014**<br>(0.006)|-0.020***<br>(0 .007)|-0.012**<br>(0.006)|
|GDP cap ratio||-0.177<br>(0.749)|-0.090<br>(0.767)|-0.043<br>(0.724)|
|Constant|-32.023**<br>(14.640)|-31.434**<br>(13.789)|16.330***<br>(4.572)|-32.016**<br>(14.941)|
|Observations|320|314|314|314|
|R-squared|0.0995|0.134|0.093|0.1453|

Notes: Robust standard errors are in parenthesis, where *** is significance at the 1% level, ** significance at the 5% level, and * significance at the 10% level.

The analysis of the control variables also provided interesting results. Starting with the climatic variables, we find evidence that average BAR score increases as a function of climatedriven water variability. While this may not be in line with common arguments regarding the links among environmental change, international security, and conflict, it is in line with previous scholarship that has found a robust relationship among water variability, scarcity, and cooperation (Tir and Ackerman, 2009; Dinar et al., 2010). However, similar to the results reflected in Dinar et 16
Recall that, under the alternative hypothesis, _γ_ = _φ/ψ_ , which is the ratio of the wageprice elasticity implied by segmented labor markets to that implied by unsegmented markets. As seen in Table 1, _γ_ averages 0 _._ 376 if income effects on the demand for services are operative and zero otherwise. What is the likelihood that I would have detected a deviation away from the null (towards zero) as large as _γ_ = 0 _._ 376? Panel (c) of Figure 2 shows the power function for _γ_ based on the column 5 specification, indicating around a two-thirds chance of rejecting _H_ 0 : _γ_ = 1 at _γ_ = 0 _._ 376. In other words, the wage-price elasticities implied by the two alternative models are not sufficiently far apart to be distinguishable with near certainty in my data.

By contrast, if the demand for services was unresponsive to changes in income induced by higher agricultural prices over the five-year period in question (for reasons discussed at the end of section 2.1), then _ψ_<sup>_SR_</sup> is the relevant null elasticity. Reestimating the column 5 specification with ∆ _wd/ψd_<sup>_SR_</sup> as the dependent variable then leads to the � _γ_ reported in column 6 and the corresponding power function in panel (d) of Figure 2. The upshot is that the alternative of interest in this case is _γ_ = _φ_<sup>_SR_</sup> _/ψ_<sup>_SR_</sup> = 0, against which my test has high power (a 95% probability of rejection). Thus, provided that these income effects indeed did not materialize, my data allow me to finely discriminate a model of perfect intersectoral labor mobility from one of segmented markets.

# **3.8 Robustness: NREGA**

India’s National Employment Rural Guarantee Act (NREGA), passed in 2005, provides every rural household with 100 days of manual labor at a state-level minimum wage, which is typically above the market wage. Phased in nationally beginning in 2006, NREGA, by official counts, provided employment to 53 million households in 2010-11 over and above existing public works programs. Imbert and Papp (2012), using NSS-EUS data and exploiting the gradual phase-in of the program, find that NREGA increased overall public works employment while (modestly) raising private-sector wages in rural India. Since these 20
47 Women. The 1991 Pakistan intgrated housedd mrvey egimats nra fmalmued **houedxii** at 1.7 psooI of houIeolds. This figr sems _low,_ any cawe it iid not corram rdect the coustro **_aof_** wmen to houseid income. More recet woiC<sup>7</sup> based on the 1989 AERC/Wodd Bank study dirctly eimes the **cedibdm** of **ibio** household income at 13.3 peraz Extrapol&*g from this _gr,_ it is ammaed that 1S percent of the agrtual GDP (cmtywide) of the poorest 40 peet of **_nur_** hohods **ia** uitor d by **wome** Thus, won shoud **rceid** at lea 15 pc entof theredit targeted at the nual poor or aboutRs. 1.7 billic The total resources noeded fx targeted nual acdit are esimated at Rs 12.8 billion 1 tmam the amou (b 15.5 billion) these instiutons a ty lnd **to** agricltr in their untr_ed p gramL At the 1993 lewI, the 1,n of Rs. 2.7 billion wuld be available to reire bad debt and make the insut financly viabeir tgd _kvAn_

# Table 4.2. Annual PoLicy-Directed Rura Credit Neod Under Low-Gwt e (1993 Rs. Billion)

<!-- Start of picture text --> Taret group or popose Low growh scenio<br>Tdtal.targeted rnral crdit 12.8<br>Smallholders 6.2<br>bwestmen 4.2<br>Prodution 2.0<br>Consumption 2.7<br>M clawnwrprises 2.2<br>women 1.7<br>Total 12.8<br>Sun Aos'n<br><!-- End of picture text -->

# teon adations for the Exiting acal ht_ oms

The major instimional players in nual finance are the commercial bmn the coopeau socies **and** bub, and the Agricultual Deveopme Bank of Paksan (ADBP). Due to vaiu misrectd poicy irnt u_M theme institutions do not operat efficientlyt for imprng the opertons of each of then pmipa ae made below.

'Eshya Mujabid-Mukht **a** r nd Noor-ul-Hassan (1992). "Female Participation in Huehold Activities: Some Asseument of the Socio-econonuc and Cultural hnpact", Pakistan )c gment m ie (Winter).
||Jawand<br>Muqur<br>Qadis|700<br>1686<br>763|0<br>0<br>0|
|---|---|---|---|
||Qala-e-Naw|4615|1790|
|July-Dec 2020|Ab Kamari|909|0|
||Bala Murghab|11480|0|
||Ghormach|264|0|
||Jawand|1461|0|
||Muqur|2002|0|
||Qadis|1029|0|
||Qala-e-Naw|7674|6072|

# Changes in the numbers of Arrival IDPs (stock of IDPs) in 2018-2020

In 2020, the number of IDPs (protracted IDPs) in the Badghis province decreased by 24,327 compared to 2018 (Table A8). In particular, this effect was achieved due to IDPs leaving Qala-e-Naw (-67,888) and Ab Kamari (-693) districts. In other districts, the numbers of IDPs increased as compared with those in 2018. These numbers should be interpreted with caution as the government of Afghanistan disputes IOM’s numbers of protracted IDPs.

Table A8. Changes in the numbers of IDPs in 2020 vs. 2018 according to IOM data

|Districts|2018|2020|Absolute change|Percentage change|
|---|---|---|---|---|
|Ab Kamari|7,806|7,113|-693|-8.9|
|Bala Murghab|9,985|44,118|34,133|341.8|
|Ghormach||1,146|1,146|-|
|Jawand|3,319|6,138|2,819|84.9|
|Muqur|7,203|12,319|5,116|71.0|
|Qadis|8,069|9,109|1,040|12.9|
|Qala-e-Naw|233,900|166,012|-67,888|-29.0|
|Total|270,282|245,955|-24,327|-9.0|
In contrast, average net revenues in rainfed villages were only 7464 Yuan per hectare (995 USD), more than 25 percent lower than average.

Simple statistics indicate that there is possibly some relationship between climate and net crop revenue. In Table 2, we group farms by net revenue. Farms with higher net revenues tend to have higher temperatures and more rain. For example, the twenty percent of farms with the lowest net revenues had annual temperatures of 8.2<sup>o</sup> C and annual precipitation of 595mm. In contrast, the twenty percent of farms with the highest net revenue had temperatures of 15.8<sup>o</sup> C and precipitation of 1152 mm. This positive association between net revenue, precipitation, and temperature applies to both rainfed and irrigated farms.

Table 2, of course, does not control for many factors that might vary from farm to farm. In order to do a more complete analysis, we must control for these factors. It is also important to do a more thorough job of exploring the role of seasonal variation in climate. We therefore turn to the Ricardian regressions to do a more thorough analysis of how climate and other factors affect net revenues.

# **Ricardian Regression Analysis**

In Table 3, we explore a regression model of net revenue per hectare on climate, soils, and a number of farm variables. We examine this regression for three samples: all farms, farms that are irrigated, and farms that are rainfed (no irrigation). Note that there are 8405 farms in the full sample, there are 2750 irrigated farms, and there are 2119 rainfed farms. There are approximately 3500 farms in villages with a mix of rainfed and irrigated farms where we cannot determine whether the farm is irrigated or not. The goodness of fit measures (adjusted R<sup>2</sup> ) for all of the models range from 0.17 to 0.26, a level that is relatively high for cross sectional household data<sup>3</sup> .

The analysis of all farms shown in the first column in Table 3 reveals that many of the control variables are highly significant. Clay and silt soils increase net revenues per hectare (compared to sand). It is advantageous for a farmer to be on a plain, have access to a road, and participate in a production association. It is disadvantageous for a farm to be a larger size or

> 3 The adjusted R2 of our estimation results are also similar to that in other countries, for example, in the research of Africa (Kurukulasuriya and Mendelson, 2006), the adjusted R<sup>2</sup> is 0.35; for Brazil and India, it is 0.40 and 0.56 separately (Mendelson, et.al., 2007).

11
_Section 3_ _Presumption in favour offree entry_ cover new areas. A Board member may demand kick-backs on major contracts, even through this increases costs to the final consumer.<sup>19</sup> Even nominally 'independent' regulators can have their own agendas and interests to pursue.

A study of 58 countries found that, across the economy as a whole, _corruption and bureaucratic efficiency indices are significantly and robustly negatively correlated with investment even controlling for other determinants of investment, including the political stability index"_ and that _"the bureaucratic efficiency index is significantly and robustly associated with low growth_ ... _there is evidence that institutional inefficiency causes low growth"_ (Mauro, P (1995), _Corruption and Growth_ Quarterly Joumnal of Economics, August 1995 p.<sup>681</sup> -<sup>7</sup> 12, p.695) The study also found a strong correlation between the level of red tape and the level of corruption. It has been suggested that bureaucrats intentionally introduce new regulations and red tape, in order to be able to extract more bribes by threatening to deny permits.<sup>20</sup> Countries with the least administrative capacity failure will typically face the greatest risks of costly regulatory failure.

The costs can be extremely high. For example a study of Nigerian manufacturing<sup>21</sup> found that firms spent well in excess of 10% of their total machinery and equipment budget on their own generation, water supply and other infrastructure provision. This expenditure would have been unnecessary, had the monopoly infrastructure providers offered a reasonable service. The authors noted that the public monopoly providers "have large amounts of capital already in place but fail to deliver their service at the level required to meet demand". They concluded that, _"The causes of [public monopoly] infrastructural failure may be grouped into two kinds. The first is relatively well understood and relates to shortcomings of the technology used by the public sector, including problems in the day-to-day management, and operation and maintenance of the facilities. The second is more complex in nature and less well controlled, and relates to general problems with administration, bureaucracy, planning, metering, billing for services delivered, revenue collection, personnel training in the public sector and lack of appropriate incentives for management andpersonnel in part because of civil service pay ceilings. This_

- 19 McCarthy, Stephen (1994) Africa: The Challenge of Transformation, St Martin's Press, New York, New York; cited in Kerf, Michel and Smith, Warwick (1996) Privatising Africa's Infrastructure: Promise and Challenge, IBRD World Bank, Washington Box 1-2.

- 20 Krueger, Anne (1993) _Virtuous and Vicious Circles in Economic Development_ Papers and proceedings of the American Economics Association LXXXIII (1993), p.<sup>35 1</sup> -56; and De Soto, Hemando (1989) The Other Path New York, New York, Harper and Row; cited in Mauro (1995).

- 21 Lee & Anas (1992).

_25_
Table 5: Infant Mortality Decline by Region, 1990-98 <!-- Start of picture text --> —SsC=“~*~*é~iSC‘“‘CSSC*‘LSOUOCO<br>fen<br>Red River Delta 35 27<br>North Central 38 37<br>i<br>a<br>——Ss=“‘*‘“‘*S*S*srSC~“‘CNTMTMTM~C*dP<br>Rint COS<br>Cd<br>[vetewsSSSSC“‘ir :<br><!-- End of picture text -->

# Source: 1990: GSO (1995), 1998: GSO (2000)

Other notable features of the decline in infant mortality in the 1990s are (i) remarkable decline in infant mortality in the Northern Uplands and (ii) lack of relative progress in the three central regions: North Central, Central Coastal and Central Highlands. The Northern Uplands, one of the poorest regions, has shown an impressive decline in infant mortality. These changes represent the highest rate of decline in the country. This shows that large improvements in infant and child health are possible even in poor and remote areas. Analytical work in Vietnam has showna positive relationship between the reach of child health services and reductions in child mortality. Reduction in infant mortality and the improvements in areas like the Northern mountains can probably be largely attributed to better provision of health services including immunization services. Greater targeting of the central provinces to increase access to health services and improve quality of life appears essential.

Less progress for the poor - Declines in IMR among poor households have been considerably lower than those among the non-poor (Table 6).

Table 6: Infant Mortality Decline by Poverty Status, 1990-98 <!-- Start of picture text --> IMR<br>VLSS 1992-93 VLSS 1997-98 % Change<br><!-- End of picture text --> Source: VLSS 1993 and 1998; Note: the VLSSs show much lower IMRs than the census.

23
diminishing returns to financial investment in the education production function plays a critical role. The evidence that peer and role model effects do not generate the convexity in the mobility equation suggests more efficient investment by educated parents as a likely source of convexity as emphasized by Becker et al. (2015, 2018).

The rest of the paper is organized as follows. Section (2) provides a brief discussion on the related literature with a focus on work on developing countries, and on Indonesia in particular. Section (3) develops the extension of the Becker et al. (2015) model with selffinance constraint on financing children’s education and derives the estimating equations for intergenerational persistence and optimal parental investment. Section (4) discusses the data with a focus on the issues of sample truncation due to coresidency and the construction of a cognitive ability index using three measures of ability in IFLS 2014. The next section presents the estimates for the mobility equation. Section (6) is devoted to understanding the economic forces at work behind the rural-urban divide in educational mobility. Section (7) explores the role of peer and role model effects in the convexity of mobility equation. The paper concludes with a summary of the results and highlights the portability of the model and methods developed here to other developing country analysis.

# **(2) Related Literature**

The literature on intergenerational economic mobility in developed countries is vast, with fundamental theoretical and empirical contributions, from Becker-Tomes (1979, 1986), to Solon (1992), to Chetty et al. (2014) are some of the seminal studies. Most of the studies focus on intergenerational persistence in permanent income between father and son. An important finding from this literature is that rich panel data on income for many years from appropriate phases of the life-cycle are required to get credible estimates of intergenerational persistence in permanent income (Solon (1992), Mazumder (2005)). For excellent surveys of this literature see, Solon (1999), and Black and Devereux (2011).

The literature on developing countries is limited, with increasing interest in the last few years (see Iversen et al. (2019) and Emran and Shilpi (2019) for recent surveys). The required panel data on income are not available in most of the developing countries, and as a result, the focus of the literature has been on educational linkages across generations (see, 5
regard to Latin America, case studies of Argentina, Brazil, and Mexico from 1994 to 1999 indicate that foreign banks did not pull back from host countries in the face of their economic problems, but rather viewed these difficulties as an opportunity to become more firmly rooted in these economies (Peek and Rosengren, 2000). And in fact, in Argentina and Mexico foreign banks had higher growth rates and lower volatility of lending than domestic banks during the crises of the mid to late 1990s (Dages et al., 2000). More generally, foreign banks in Latin American countries (Argentina, Brazil, Chile, Mexico, Peru, and Venezuela) showed more robust loan growth, a more aggressive response to asset deterioration, and greater ability to absorb losses than did domestic banks during this period (Crystal et al., 2001, 2002). In Eastern Europe, domestic banks contracted their credit base during crises from 1993 to 2000, whereas greenfield foreign banks did not (de Haas and van Lelyveld, 2006). Furthermore, a study of banks and firms in 13 countries in Eastern Europe from 2000 to 2005 shows that lending relationships for foreign banks tended to be more stable than those of domestic banks in that foreign banks were less likely to drop their clients, even in the aftermath of an acquisition (Giannetti and Ongena, 2012).

No doubt in response to the global scope and severity of the 2008-2009 crisis, there has been a recent proliferation of studies analyzing credit growth during this recent episode. In particular, there is evidence that foreign banks reduced their lending earlier and faster than domestic banks during the crisis (Claessens and van Horen, 2012; de Haas and van Lelyveld, 2011), in particular within Eastern Europe (de Haas, Korniyenko, Loukoianova, and Pivovarsky,

> Using data from 45 of the largest multinational banks from 1991 to 2004, de Haas and van Lelyveld (2010) find that their lending did not slow during systemic crises in host countries, which they attribute to the support received by subsidiaries from their parent banks. Because lending by domestic banks did slow during crises, multinational banks could be viewed as stabilizing forces in turbulent times. For a more thorough review of the literature on crises and foreign bank participation see Cull and Martinez Peria (2010).

4
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