rafmacalaba/datause-encoder-data huggingface dataset:
34

**Table SI-7:** Lagged migration impacts of high damage storms

||_>_10_MM_|_>_20_MM_|_>_40_MM_|_>_80_MM_|_>_160_MM_|
|---|---|---|---|---|---|
|1(Storm year)=1|0.057|0.153<sup>_∗∗_</sup>|0.287<sup>_∗∗_</sup>|0.463<sup>_∗∗∗_</sup>|0.675<sup>_∗∗∗_</sup>|
||(0.036)|(0.058)|(0.118)|(0.128)|(0.163)|
|1(Storm year), t-1=1|0.032|0.091<sup>_∗_</sup>|0.138<sup>_∗_</sup>|0.233<sup>_∗∗∗_</sup>|0.223<sup>_∗∗_</sup>|
||(0.039)|(0.047)|(0.068)|(0.058)|(0.097)|
|1(Storm year), t-2=1|-0.009|0.007|0.010|0.061|0.030|
||(0.027)|(0.031)|(0.047)|(0.054)|(0.080)|
|1(Storm year), t-3=1|-0.022|-0.021|-0.006|0.031|0.013|
||(0.039)|(0.038)|(0.049)|(0.065)|(0.096)|
|1(Storm year), t-4=1|0.004|0.013|0.047|0.095|0.066|
||(0.035)|(0.038)|(0.056)|(0.079)|(0.112)|
|1(Storm year), t-5=1|0.056|0.073|0.110|0.140|0.069|
||(0.035)|(0.052)|(0.069)|(0.103)|(0.121)|
|N|52,514|52,514|52,514|52,514|52,514|
|Σ<sup>5</sup><br>_i_=0<sup>_Stormt−i_</sup> <sup>= 1</sup>|0.118|0.316|0.586|1.023|1.076|
||(0.146)|(0.186)|(0.290)|(0.239)|(0.327)|
|Fixed Efects:||||||
|County|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of out-migrants from a county as the outcome. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to occur - during a hurricane in the year and the total value of compensation approved by FEMA was greater than indicated in the column heading. Roughly 90% of storms in our sample result in less than 10 _millioninapprovedcompensation._ 95% _resultinlessthan_ 50 million in approved compensation. 1(Storm year), t-# indicates that the county experienced a storm # years previously.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.
# **Annex 1: Government Debt Dynamics**

Debt dynamics are driven by primary deficits and interest payments, which add up to the gross fiscal deficit. As debt is usually expressed as a ratio of GDP, growth also comes into play. Equation (1) is the standard difference equation for government debt: where _d_ is the government debt-to-GDP ratio, _pd_ is the ratio of the primary deficit to GDP, _DR_ is divestment (privatization) receipts, _F_ stands for the new flow of debt issued in connection with financial sector bailouts (both as ratios of GDP), _r_ is the real interest rate, _g_ is the real growth rate and the subscript refers to fiscal years.<sup>49</sup> The LHS of (1) is the increase in the debt-GDP ratio relative to the previous year. It is explained by the primary deficit (pd) less divestment revenues (DR - as these lower borrowing requirements) plus financial sector bailout costs (F – as these necessitate more debt issue). The last term captures the interaction between interest rates and growth: the interest rate determines the interest payment on public debt while growth acts to lower the ratio of GGD to GDP.

Key points to note about the real interest rate are that it reflects all capital gains and losses through real exchange rate changes and inflation on the outstanding stocks of foreign currency-denominated and domestic currency debt respectively; and the implicit GDP deflator is used to convert nominal to real.

# **References**

Acharya, Shankar. 2001. “India’s Macroeconomic Management in the Nineties.” Indian Council for Research on International Economic Relations, New Delhi.

- ———. 2002a. “Macroeconomic Management in the Nineties.” _Economic and Political Weekly,_ April : 1515-1538.

- ———. 2002b. “India’s Medium-Term Growth Prospects.” _Economic and Political Weekly,_ July: 2897-2906.

———. 2006. _Essays on Macroeconomic Policy and Growth in India._ Oxford University Press. New Delhi.

- Ahluwalia, Montek S. 2002a. “India’s Vulnerability to External Crisis: An Assessment.” In _Macroeconomics and Monetary Policy: Issues for a Reforming Economy. Essays in Honor of C. Rangarajan_ , eds. Montek Ahluwalia, S.S. Tarapore and Y.V. Reddy, OUP

- ———. 2002b. “Economic Reforms in India since 1991: Has Gradualism Worked?” _Journal of Economic Perspectives,_ Vol. 16, 67-88.

- Aizenman, Joshua, 2003. “On the Hidden Links Between Financial and Trade Opening.” Working Paper 9906. _National Bureau of Economic Research_ ,  Cambridge, MA.

> 49 The last term on the RHS can be equivalently written <u>(</u> _it_ − _Gt_ <u>)</u> _d t_ − 1 , where _i_ is the nominal interest rate on GGD 1( + _Gt_ )

> and _G_ is the nominal rate of growth of GDP.

29
A number of factors make India’s flagship MGNREGA program an ideal setting to study the impact of employment guarantee schemes on schooling and child labor. First, the scale of the program is massive. By its fifth year, the program provided employment opportunities to 53 million households for 2.3 billion man-days, making it the world’s largest operating employment guarantee scheme. Second, the program was gradually rolled out in the districts of India based on pre-determined characteristics measured 10-15 years prior to the program. This variation provides an excellent opportunity to evaluate the impact of this program.

Using a longitudinal data set of 1.13 million primary and upper-primary schools in India, the study compares within school enrollment across the districts which received the program early versus late. We find that, conditional on school characteristics, school enrollment grew more slowly in districts where the program was phased in early. This result is driven by primary schools rather than upper primary schools. Using nationally representative survey data, we examine the effect on children’s employment both outside and within the house. We find that children are more likely to be employed in early phase districts post treatment.<sup>2</sup> Responses to the program are heterogeneous. Responses to the program vary by age, type of school and quality of private schools within private schools. Enrollment falls for younger primary school children whereas school outcomes deteriorate for upper primary children. Grade 7 school outcomes in early treated districts get worse for both government and private schools but effects are larger for private schools. Within private schools, the effects are driven by low quality private schools.

The timing of the roll-out was not random and was largely ascertained by three district characteristics: district Scheduled Castes and Scheduled Tribes population in the 1991 Census of India, 1996-97 agricultural wages, and the 1990-1993 output per 2In many settings, children who work a significant number of days in a year are less likely to be enrolled in school (Jensen, 2000). Our results show that safety net schemes can result in an increase in this phenomenon.

2
are presented in Table 1. Column (1) estimates a sparse specification; column (2) adds household fixed effects; and column (3) adds both household and month-year effects.

Violence clearly escalated more following displacement in origin districts than for nondisplaced households following their most recent survey. Column (1) demonstrates there was an average increase of 1.58 more monthly fatalities in origin districts after displaced households fled than following the most recent survey of non-displaced households in their districts of residence. The overall increase for displaced households is large, and represents 40 percent of the monthly fatalities prior to the most recent survey of nondisplaced households in their district of residence. Furthermore, the increase is more than double the increase in the district of residence of non-displaced households following their most recent survey (1.37 monthly fatalities).

Importantly, this difference persists when accounting for regional, household, and time-specific factors. In the most complete specification estimated in column (3), the relative increase in fatalities following displacement remained nearly constant at 1.44 more monthly fatalities. However, the increase in fatalities in the district of residence for non-displaced households reduces in magnitude and becomes indistinguishable from zero when these additional factors are included.

Furthermore, we examine how violence was changing in the districts to which households fled (migration districts). Columns (4)-(6) of Table 1 re-estimate the baseline specification but use the district of migration for households that became displaced outside of their origin district.<sup>15</sup> We find that displaced households tended to flee to districts where violence was increasing similarly to the rest of the country (in contrast to escalation in their origin districts), further illustrating that violence was important to displacement decisions. In all specifications, the sign of the estimate for migration districts is the opposite of origin districts, the magnitude is significantly smaller, and the estimates are all statistically indistinguishable from zero. Importantly, given the sign and precision of the estimate, we can rule out relative increases of greater than 0.89 monthly fatalities in the most complete specification (column 6).

> 15Not all displaced households became displaced out of their origin district. Approximately 29 percent of displaced households became displaced outside of their origin districts.

13
Table 6.3—Regression results of college entry (linear probability model), for full sample and trimmed and weighted sample

|||Full S|ample||Trim|med and W|eighted Samp|le|
|---|---|---|---|---|---|---|---|---|
||Model 1||Model 2||<br>Model3||Model 4||
|TEEP cohort*TEEP province|-0.230|(-1.18)|-0.306|(-1.55)|-0.355*|(-1.67)|-0.395*|(-1.89)|
|Female|0.0442|(0.34)|0.0540|(0.42)|0.0979|(0.72)|0.100|(0.73)|
|Female*TEEP cohort|-0.0350|(-0.77)|-0.0372|(-0.81)|-0.0357|(-0.71)|-0.0357|(-0.71)|
|Female*TEEP cohort*TEEP province|0.0983**|(2.28)|0.0992**|(2.31)|0.0893*|(1.82)|0.0892*|(1.82)|
|Log(consumer durables)|0.0561***|(4.58)|0.0544***|(4.17)|0.0651***|(5.32)|0.0640***|(4.97)|
|TEEP cohort|-0.250|(-1.27)|-0.241|(-1.19)|-0.222|(-1.16)|-0.217|(-1.11)|
|TEEP cohort*Log(consumer durables)|-0.0216|(-1.28)|-0.0210|(-1.19)|-0.0259|(-1.55)|-0.0253|(-1.46)|
|Female*Log(consumer durables)|0.00590|(0.52)|0.00536|(0.47)|0.00128|(0.11)|0.00117|(0.10)|
|<br>TEEP cohort*TEEP|||||||||
|province*Log(consumer durables)|0.0157|(0.89)|0.0203|(1.15)|0.0257|(1.32)|0.0281|(1.47)|
|Age|0.303***|(3.77)|0.297***|(3.54)|0.275***|(2.77)|0.277***|(2.77)|
|Age squared|-0.00933***|(-4.48)|-0.00915***|(-4.22)|-0.00876***|(-3.41)|-0.00880***|(-3.40)|
|Mother’s years of schooling|0.0340***|(10.79)|0.0339***|(10.89)|0.0324***|(9.18)|0.0324***|(9.23)|
|Mother’s age|0.00371***|(3.37)|0.00370***|(3.35)|0.00369***|(2.97)|0.00368***|(2.91)|
|Number of siblings|-0.0129***|(-4.05)|-0.0128***|(-3.94)|-0.0124***|(-3.50)|-0.0123***|(-3.36)|
|Poverty incidence rate|||0.00275|(0.89)|||0.00259|(0.82)|
|Infrastructure index|||-0.00935|(-0.11)|||-0.00107|(-0.01)|
|Number of students per classroom in high|||||||||
|<br>school|||-0.000559|(-1.32)|||-0.000296|(-0.55)|
|If phone interview|||-0.0499*|(-1.88)|||-0.0353|(-1.05)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Percentage of predicted values out of|||||||||
|<br>[0,1]|5.10%||5.00%||6.60%||6.20%||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000. t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

39
disadvantaged with respect to public schooling benefits in rural Pakistan. The situation could be improved with little cost to productivity by targeting schooling quantity and quality improvements to the disadvantaged groups. The Pitt and others investigation emphasizes the difficulties in econometrically establishing _ex post_ the impact of public expenditures. Once the authors have controlled for fixed effects and the nonrandomness of program placement (again for Indonesia), with one or two exceptions, few programs are evidenced to have much direct impact on health and education outcome variables. The last study highlights some of the severe difficulties that make the estimation of public investment impacts so elusive.

- 3.2.3 Lessons from incorporating behavioral responses in incidence studies

The key lessons here are ones already evident from the behavioral approaches discussed above. Behavioral responses can be of consequence to conclusions about the distributional impacts of policy interventions. In a number of the empirical case studies included in this review, efforts at reproducing the relevant counterfactual have been used to inform policy evaluations. Accounting for foregone incomes in assessing the poverty impact of public employment schemes is found to be of importance in Ravallion and Datt's study in PSP; it estimates that foregone incomes in two villages participating in the Employment Guarantee Scheme (EGS) of India's Maharashtra state account for about one quarter of total wage earnings on the scheme. Labor market participation responses to the rice subsidy discussed by Alderman and Sahn have marked repercussions for the scheme's net transfer benefits. Sri Lanka's targeted food stamp scheme is estimated to have resulted in a fall in labor market participation of as much as 2.5 days per month for males and 2.9 days for females. Finally, the public provision of social security is estimated to result in substantial displacement of the private moral economy that exists between households in the Philippines (Cox and Jimenez, PSP).

27
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Policy Research Working Paper 10241

# **Abstract**

This study seeks to support the efforts of Sudan in the targeting of the Sudan Emergency Safety Nets Project (SESNP) and any other future safety net programs in Sudan to those most in need. Sudan has faced numerous challenges for several years. Since its separation from South Sudan in 2011, the economy has experienced macroeconomic imbalances that have lasted for over a decade. In addition, the country has faced political instability, internal conflicts, and challenging climatic conditions. All of these challenges pose direct consequences to the poor. More recently, the onset of the COVID-19 pandemic and the Russia-Ukraine crisis have only exacerbated the economic and social situation in the country. In this context, the number of poor and vulnerable is expected to have increased considerably since the last reported official poverty rate, which accounted for 61.1% of the population in 2015. In light of these events, there is a growing consensus on the need of social safety net programs in the country. The proposed program, the SESNP, is expected to provide unconditional cash and food transfers to nearly 2 million Sudanese people (i.e., about 5% of the population). To support this program in targeting beneficiaries to improve the poverty impact of the program, we develop a Proxy Mean Tests (PMT) for Sudan based on the National Household Budget and Poverty Survey (NHBPS) 2014/2015. The results indicate that the use of a PMT could considerably improve the program in reaching those most in need, while reducing expenditure towards those with adequate resources. This could improve both the poverty impact, as well as the sustainability of the program.

This paper is a product of the Poverty and Equity Global Practice. 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 aetangndip@worldbank.org; paervin@gmail.com; snamara@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
and 2013 from the national I-O tables, we applied a nonsurvey-based method (Miller and Blair 2009; Flegg and Tohmo 2011; Kowalewski 2013). Appendix C discusses how we constructed the regional I-O tables.

# **Sectoral Domar Weights and Value-Added Shares**

Appendix figure A3 (panel a) shows changes in Domar weights, which is the ratio of sectoral output to GDP, between 2004 and 2013. In the south, production activities primarily shifted from agriculture and “other services” to mining and “other industry.” The Domar weight increased the most for mining (17 percentage points), followed by “other industry” (6 percentage points). In the north, the Domar weight for agriculture decreased by 36 percentage points. “Other industry” experienced the largest increase in the Domar weight (by 16 percentage points), followed by mining (10 percentage points) and WRT services (10 percentage points).

Appendix figure A3 (panel b) shows the change in value-added shares for each sector between 2004 and 2013. The regional pattern of the change in sectoral value-added shares is comparable to the regional pattern of the change in Domar weights. In the north, the gain in the value-added share in “other industry” (23 percentage points) is larger than the change in the Domar weight in “other industry” (16 percentage points). At the same time, the gain in the value-added share in mining in the south (13 percentage points) is lower than the Domar weight in mining (17 percentage points). The implications of changes in the Domar weight and the sectoral value-added share for intersectoral linkages are twofold. First, “other industry” continues to be the most important sector in the production network of the north. Second, mining plays a dominant role in the production network, especially in the south.

# **Intermediate Input Use and Input Downstreamness Index**

We compute the intermediate use share as the percentage of sectoral output used as intermediate inputs for other sectors, and its change between 2004 and 2013. Appendix figure A4 (panel a) compares the change in the intermediate share across sectors. In both the north and the south, the share of intermediate use in mining has increased by 11 and 13 percentage points, respectively. In the south, the change in the intermediate use in WRT services (by 32 percentage points) is more the four times the same in the north (by 7 percentage points).

We next compute downstreamness (Miller and Blair 2009; Antras et al. 2012; Antras and Chor 2013) at the sector level for 2004 and 2013 using the national and regional I-O tables. The downstreamness index measures the average distance from primary input 11
# **1. Introduction**

While there is a large literature on the investment and productivity effects of land rights formalization, the impact of individuals’ tenure security under traditional regimes has received little attention. This is a key knowledge gap because most of Africa’s rural land is managed under customary tenure and long-term rights by vulnerable groups such as women, migrants or herders may be the first to be attenuated in view of increasing land scarcity. To help fill this gap, we use unique survey data from Malawi to show that individuals’ long-term rights of bequest and sale significantly affect investment and cash crop adoption; that the gender of right holders matters, together with local institutional arrangements; and that effects may be obscured by measurement error associated with traditional data collection methods. We draw implications for policy and research in Malawi and beyond.

How individuals can access land or exercise their land rights has far-reaching implications for their well-being and ability to take advantage of economic opportunities. While formally documented rights are the norm in developed countries, across the developing world -especially in rural Africaformalization remains out of reach for the majority of the population. Instead, land is almost exclusively accessed through customary arrangements that are administered by traditional authorities. Individuals’ land rights in such systems depend on societal status (Honig 2017) and relationships with local leaders (Goldstein and Udry 2008).

The literature has long highlighted that, as long as land is relatively abundant, customary systems’ ability to flexibly adjust to changes in external conditions allows use of land as a social safety net (Andolfatto 2002) and for mutual insurance (Beck _et al._ 2019). Where risk is high and other mechanisms for insurance are absent, this is a key advantage (Bruce and Migot-Adholla 1994) that may well lead land owners to prefer traditional arrangements to more formal alternatives (Atwood 1990).

Yet, once land becomes scarcer, conflicts that are difficult for traditional authorities to resolve in a predictable way may exacerbate insecurity (Eck 2014) and leaders may act in their own, rather than the group’s, best interest (Greiner 2017). This can lead to a divergence between private and social benefits so that land may be privatized even where this reduces overall social welfare (Leeson and Harris 2018) or a shift in land rights within the household in ways that may disadvantage wome <mark>n. Growing land scarcity,</mark> possibly exacerbated by shifts in land demand due to exogenous shocks such as the 2007/08 food and financial crisis or the COVID-19 pandemic may thus gradually erode the rights of traditionally disadvantaged groups such as women, migrants, and pastoralists. These changes may affect not only equity but also efficiency, by impacting investment incentives and the ability to transfer land to better users.

2