rafmacalaba/datause-encoder-data huggingface dataset:
While there were ex-ante concerns that a randomized top-down voucher allocation might be perceived as unfair by participants, block-level public lotteries were deemed to be too impractical and problematic for ensuring balance and compliance. To balance these two concerns, we performed the voucher randomization in the following manner for each block:

1. We randomly drew a distribution of general/conditional voucher pairs, repeating the draw 100 times.

2. Balance was then tested for each draw using a vector of observable parcel-level characteristics and the three draws that were the most balanced (defined by _average_ t-stat values) were kept.

3. These three outcomes were then presented to residents at the block-level information sessions. Each attendee was made aware of the three possible distributions, each labeled with a designated number. One of the attendees was selected by the rest to draw a number out of a hat, each number corresponding to a voucher distribution outcome. Whichever number was chosen determined the draw that would be used for the voucher distribution.

Thus we were able to maintain control over the broad aspects of the randomization while still allowing residents some perceived agency in choosing the outcome. Following the voucher distribution, households were free to sign up with WAT and begin repayment.

Both the block and the parcel-level randomizations in Barafu and Kati were performed at different times and thus represent independent draws. Due to delays in the government provision of the maps necessary to identify treatment and control households, the program was first introduced in Barafu in late 2010, but not in Kigogo Kati until approximately a year later. In Barafu, block-level information and voucher sessions were held in late October, 2010, with participating landowners paying their net price to WAT between November and the summer of 2011. Following repayment, landowners in Barafu have been filling out and turning in CRO applications, to then be checked and sent on to the local government by WAT. In Kigogo Kati, the voucher sessions were held in early November, 2011, with repayment continuing until the summer of 2012. Due to excessive flooding in Kati, overall participation and take up has been significantly lower than in Barafu. The data presented in this paper comprises the latest take up and application data available from the project.

# **3.2 Balance and summary statistics**

Table 3 shows summary statistics for a select group of baseline characteristics, as well as a series of balance tests. To test whether there is a significant correlation between assigned voucher values and baseline characteristics, we estimate the following specification for each characteristic using ordinary least squares: 11
<!-- Start of picture text --> Simulated Decline of Output<br>0.08 - - - - - - - - -AI<br>p 0.70 _ - _,_ _ - - - - - - - EtcedGP 921<br>B<br>B0 XL0 2 -------<br>0<br>86 s8 90 92 94 96 98<br>GDP in a Changing Economy (Base Year = 100)<br><!-- End of picture text --> Flgure **7: Impact of** Trade **Shocks** on **Output** output and revenue decline in Eastern Europe is duplicated in Figure 7 and 8, respectively. Real output is expected to decline by about 8 % GA). Tax revenue, on the other hand, is expected to fall by 22 % **(u).9** The effects of uncertainty in the outcomes are reflected in several ways. A straightforward statistical interpretation indicate that the risk of output deviating ± 2.2 percentage points {one standard deviation) around the mean is 68.3 percent. The chance of output declining anywhere between 8 to 14 percent (bottom half of the distribution) is 50 percent. The average deviation of the revenue fall is higher than output. The likelihood of revenue falling by ± 4.2 percentage points (la) around the expectation is about two-third and by ± 8.4 percentage points (2a) is about 95%. There is an even risk that tax revenue rnay fall from 22 to 31 percent.

The scenario above assumes that real wage is flexible and labor is free to move from the collapsing sector to the rest of the economy. This assumption leads to the following - net output will increase by 7.8 percent (p) ± 1.6 percent _(a)_ while exports to non-CMEA region will rise by as much 21.8 percent (U) 'Tax revenue is reported in real terms. See Section 4.2, Case I, for definition and discussion.

_Go: Revenue Uncertainy 15_
The comparison of the international poverty rates using the US$1.90 per capita per day (in 2011 PPP exchange rate) poverty line, in line (d), reveals an overall decline in poverty rates in both the HBS and NPS, even though the HBS shows a much greater decline in poverty and the NPS continues to show a slight increase in poverty between the first two rounds. It is worth noting that the estimation of the international poverty rates follows the Povcalnet method which does not adjust the consumption values for spatial cost of living differences and which seems to partly resolve the mismatch in poverty trends between the two surveys.

Based on these findings, it seems that despite the differences between the HBS and NPS in survey design and methods of consumption data collection, the discrepancies in poverty levels and trends between both surveys are mainly resulting from the differences in: 1) the methods of calculation of food and basic needs poverty lines; 2) the inter‐year price deflators; and 3) to a lesser extent, the spatial price deflators.

# **V. Comparison of Inequality and Distributional Patterns between HBS and NPS**

This section compares the inequality indicators between the HBS and NPS surveys and performs the unconditional quantile decomposition to examine the specific household attributes that contribute to the changes of consumption over time in both the HBS and the NPS.

# **A. Inequality indicators**

As stated in the first section, HBS and NPS show different inequality trends, declining in the HBS and increasing across the NPS waves. The adjustments above are relevant for the poverty estimates only and cannot help in addressing the inequality discrepancies.

Table 14 presents a more complete analysis that includes Gini indices of nominal and real per capita household consumption as well as the Palma index. The inequality trend in per capita nominal consumption does not change when measured with the Gini or the Palma index. However, the increasing trend in the NPS from 2008/09 to 2012/13 is more apparent when real values are used.

**Table 14: Gini index**

||**H**|**BS**||**NPS**||
|---|---|---|---|---|---|
||**2007**|**2011/12**|**2008/09**|**2010/11**|**2012/13**|
|**Inequality**||||||

40
10 In order to arrive at an estimate of the income terms-of-trade loss, it is necessary to decide how to value the TR surplus that existed in 1988. One can note from ablu 1 that Hungary had a surplus in its bilateral mercahndise trade with the Soviet Union of 0.4 billion TR in 1988. The surplus was about the same in 1989, but rose to over 0.6 billion TR in 1990.

One extreme is to assume that the TR surpluses are valueless, i.e., that the export and import bundle of goods was the best deal that Hungary could negotiate with the Soviet Union, and Hungary would never receive any compensation in goods or services for these surpluses. Then $0.8 billion is the estimate of the income terms-of-trade loss, because it values the TR surpluses at zero.

At the other extreme is to assume that the TR surpluses would have been redeemable in full for goods and services from the Soviet Union at relative prices for imports prevailing in 1988. Under this assumption it is necessary to assign a dollar value to the TR surplus at the relative prices that prevailed in 1988. This is appropriately done by counterfactually reducing the vector of export quantities and increasing the vector of import quantities equiproportiorately such that trqde is balanced in TR. Assessing the total dollar value of these counterfactually produced vectors of exports and imports yields an expected value for the dollar loss from the switch-over of $1.23 billion.'° Which of these estimates is closer to being correct? The great uncertainty, which prevailed in 1989 and early 1990, of if and when the TR surpluses would be exchanged for goods in the future suggests that the TR surpluses were not valued in full at the relative prices prevailing in 1988, so the estimate $1.23

> t0 Hungary's bilateral trade balance with the Soviet Union is likely to be negative in 1991. Since we are estimating terms-oftrade effects, we ignore quantity effects which would affect the trade balance.
# **Migration, Economic Crisis and Child Growth in Rural Guatemala: Insights from the Great Recession**

Calogero Carletto<sup>a</sup> John A. Maluccio<sup>b*</sup> Savant Man Shrestha<sup>b</sup> Mackenzie F. Stewart<sup>b</sup> a The World Bank, 1818 H Street NW, Washington DC 20433, USA gcarletto@worldbank.org Tel. +1 202 473 1377 b Middlebury College, College Street, Middlebury VT 05753, USA Tel. +1 802 443 5941; fax: +1 802 443 2084

*Corresponding author (maluccio@middlebury.edu)

## **Keywords** : Migration, child growth, stunting, 2008 Great Recession, Guatemala

**Acknowledgments** : Data collection was supported by the Regional Program for Food and Nutrition Security in Central America, the World Bank, and the Food and Agriculture Organization, and done by the _Centro de Estudios y Documentación de la Frontera Occidental de la Frontera_ (CEDFOG) in 2008 and Khanti Consulting in 2012. Funding for the research from the World Bank and for qualitative work from a Fulbright- Hays Faculty Research grant is gratefully acknowledged. We thank participants at the 7<sup>th</sup> International Conference on Migration and Development (Oxford), NEUDC (Boston University), Universidad Francisco Marroquin and the Midwest International Economic Development Conference (University of Minnesota) including Steven Stillman, Phillip Ross and Marlon Seror and are especially grateful to three anonymous referees. All errors and omissions are our own.
Gollin, Douglas, Stephen L. Parente, and Richard Rogerson. 2002. “The Role of Agriculture in Development.” _AEA Papers and Proceedings_ 92, 2: 160–64.

Gomanee, Karuna, Sourafel Girma, and Oliver Morrissey. 2005. “Aid and Growth in Sub-Saharan Africa: Accounting for Transmission Mechanisms.” _Journal of International Development_ 17: 10551075.

Hansen, Henrik, and Finn Tarp. 2001. “Aid and growth regressions.” _Journal of Development Economics._ 64, 2: 547-570.

Holden, Stein, Bekele Shiferaw, and John Pender. 2005. _Policy Analysis for Sustainable Land Management and Food Security in Ethiopia: A Bioeconomic Model with Market Imperfections._ Research Report 140. Washington, DC: International Food Policy Research Institute.

Jayne, T.S. and Shahidur Rashid. 2013. “Input subsidy programs in sub-Saharan Africa: a synthesis of recent evidence.” _Agricultural Economics._ 44, 6: 547-562.

Lofgren, Hans, Martin Cicowiez, and Carolina Diaz-Bonilla. 2013. “MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis.” In Dixon, P B and D W Jorgenson (Eds.) _Handbook of Computable General Equilibrium Modeling._ North Holland: Elsevier. pp. 159-276.

Lofgren, Hans, Rebecca Lee Harris, and Sherman Robinson. 2002. _A Standard Computable General Equilibrium Model in GAMS_ . Washington, DC: International Food Policy Research Institute.

Marenya, Paswel P., and Christopher P. Barrett. 2009. “State-Conditional Fertilizer Yield Response on Western Kenyan Farms.” _American Journal of Agricultural Economics_ 91, no. 4: 991–1006.

33
elderly suffer from greater cognitive, gender and education gaps than other groups. As a result, they are less likely to have a savings account, own shares in a company, or have private pensions. Lusardi and Mitchell (2011; 2014) report that financial literacy is a significant determinant of retirement planning for all age groups, with elderly people suffering from high levels of financial illiteracy.

Recent research by Chen (2013) identifies a cultural component in saving and retirement planning behavior, finding that speakers of languages which grammatically associate the future and the present save more and retire with more wealth. In addition, Duflo and Saez (2002) find evidence of peer effects in retirement plan participation decisions. Duflo and Saez (2003) present quasi-experimental evidence that an individual’s decision about how much to save for retirement is affected by small changes in their environment and that network effects impact participation decisions. Instead of education, Benartzi and Thaler (2007) propose a method of influencing decisions about retirement saving plans based on the principles of "automatic enrollment". This involves changes in plan design, sensible default options, and opportunities to increase savings rates and rebalance portfolios automatically. Such design features help less sophisticated investors while maintaining flexibility for more knowledgeable participants.

Recent evidence suggests that financial security for the elderly and economic growth are best served when governments adopt three pillars of old age security:<sup>6</sup> (i) a publicly managed pension system with mandatory participation and the limited goal of reducing poverty among the elderly; (ii) a privately managed mandatory savings system; and (iii) voluntary savings. The first pillar caters to redistribution, the second and third cover savings, and all three co-insure against the many risks of old age.<sup>7</sup> Spreading the insurance function across all three pillars creates greater income security for the old, and provides greater insurance than any single system. Valdés-Prieto (2002a; b) reviews the potential costs and benefits of “third-pillar” prograss, defined as those that use incentives to promote voluntary saving for old age. Among the benefits of voluntary saving are the relative ease of accessing and mobilizing funds and the prevention of potential labor market

> 6 See Holzmann (2012) and Pallares-Miralles et al. (2012) for reviews of international pension systems. 7 The rationale is that by separating the redistributive function from the savings function, the public pillar, as well as the size of the payroll tax needed to support it, can be kept relatively small, thus avoiding many of the growth-inhibiting problems associated with a dominant public pillar (World Bank, 1994).

6
|P128096|2013|2014|4/24/2014|3 Closed|Pakistan|
|---|---|---|---|---|---|
|P128096|2013|2015|11/11/2014|4 Closed|Pakistan|
|P128096|2013|2015|6/18/2015|5 Closed|Pakistan|
|P128137|2011|2012||0 Closed|Senegal|
|P128137|2011|2013||0 Closed|Senegal|
|P128137|2011|2020|5/20/2020|1 Closed|Senegal|
|P128140|2013|2013||0 Closed|Mauritius|
|P128140|2013|2014|10/28/2013|1 Closed|Mauritius|
|P128140|2013|2014|6/26/2014|2 Closed|Mauritius|
|P128142|2012|2012||0 Closed|Europe and Central As|
|P128142|2012|2013||0 Closed|Europe and Central As|
|P128143|2012|2012|5/15/2012|1 Closed|Somalia|
|P128143|2012|2013|5/15/2012|1 Closed|Somalia|
|P128150|2012|2012||0 Closed|Romania|
|P128150|2012|2013|11/19/2012|1 Closed|Romania|
|P128150|2012|2015|3/24/2015|2 Closed|Romania|
|P128150|2012|2016|12/22/2015|3 Closed|Romania|
|P128150|2012|2017|9/26/2016|4 Closed|Romania|
|P128178|2012|2012||0 Closed|Timor-Leste|
|P128178|2012|2013|12/25/2012|8 Closed|Timor-Leste|
|P128178|2012|2013|1/31/2013|9 Closed|Timor-Leste|
|P128178|2012|2014|7/30/2013|10 Closed|Timor-Leste|
|P128178|2012|2014|1/25/2014|11 Closed|Timor-Leste|
|P128201|2013|2013||0 Closed|Bhutan|
|P128208|2012|2012||0 Closed|Sierra Leone|
|P128208|2012|2013|11/13/2012|1 Closed|Sierra Leone|
|P128208|2012|2013|6/18/2013|2 Closed|Sierra Leone|
|P128208|2012|2014|11/30/2013|3 Closed|Sierra Leone|
|P128208|2012|2014|4/25/2014|4 Closed|Sierra Leone|
|P128208|2012|2015|8/20/2014|5 Closed|Sierra Leone|
|P128208|2012|2015|5/14/2015|6 Closed|Sierra Leone|
|P128208|2012|2016|1/15/2016|7 Closed|Sierra Leone|
|P128208|2012|2016|6/16/2016|8 Closed|Sierra Leone|
|P128208|2012|2017|1/2/2017|9 Closed|Sierra Leone|
|P128208|2012|2018|8/15/2017|10 Closed|Sierra Leone|
|P128208|2012|2018|2/14/2018|11 Closed|Sierra Leone|
|P128212|2012|2012||0 Closed|Bosnia and Herzegovin|
|P128212|2012|2013||0 Closed|Bosnia and Herzegovin|
|P128212|2012|2015|1/7/2015|1 Closed|Bosnia and Herzegovin|
|P128212|2012|2016|3/21/2016|2 Closed|Bosnia and Herzegovin|
|P128212|2012|2017|6/23/2017|3 Closed|Bosnia and Herzegovin|
|P128212|2012|2018|12/29/2017|4 Closed|Bosnia and Herzegovin|
|P128239|2012|2012||0 Closed|South Sudan|
|P128239|2012|2013|11/30/2012|1 Closed|South Sudan|
|P128239|2012|2013|6/26/2013|2 Closed|South Sudan|
|P128239|2012|2014|12/25/2013|3 Closed|South Sudan|
|P128239|2012|2014|6/30/2014|4 Closed|South Sudan|
# **Kenya’s Quest for Growth Stabilization and Reforms—But Political**

**Stability?**<sup>**1**</sup>

# **April 28 2008**

Luca Bandiera, Praveen Kumar and Brian Pinto<sup>2</sup> .

> 1 This title is a take-off on “Mexico: Stabilization, Reform and No Growth” (Dornbusch and Werner 1994). With growth picking up in 2003, Kenya appeared to be one step ahead.  But it will need to make a fresh start to overcome the disruption and distrust engendered by the December 2007 Presidential elections. 2 The authors are all at the World Bank.  This paper is based on background analysis for a Country Economic Memorandum being prepared on Kenya.  We thank Viktoria Hnatkovska, Mohan Krishnaswami, Claudio Raddatz and Terry Ryan for helpful discussion and comments.  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.
poor. This survey of 1,769 households was randomly drawn from 87 villages of 29 _upazilas_ in rural Bangladesh.<sup>6</sup> These households were revisited in 1998/99. Among the 1,769 households surveyed in 1991/92 survey, 131 could not be retraced in 1998/99, leaving 1,638 households available for the re-survey. The attrition rate is therefore 7.4 percent. The 1998/99 survey included also new households from both the original survey villages and newly included villages, for a total of 2,599 households (2,226 from the original villages and 373 from the new). Among the households in old villages, 279 households were newly-sampled households and 1,947 were original panel households. The number of panel households surveyed in 1998/99 (1,947 households) was more than that surveyed in 1991/92 (1,638 households) because some of the original households split after the first survey to form new households.

The households were resurveyed again in 2010/11 with the help of the Institute of Microfinance (InM). The resurvey tried to revisit all the households (2,599) surveyed in 1998/99. However, due to attrition, only 2,342 households were identified. The attrition rate during the third round survey is about 10 percent. Due to household split-off we ended up interviewing 3,082 households in 2010/11 with 740 households split off during this period.

There is a total of 1,509 households common to all three surveys and the analysis of this study is restricted to these households only. Table 1 shows the participation status in various programs in three surveys. Membership grew steadily for all programs with the exception of BRDB, a government program, which lost a good share of its members due to reorganization between the second and third survey waves. Grameen Bank is the largest among all MFIs, and its membership among survey households increased from 8.7 percent in 1991/92 to 15.1 percent in 1998/99, and then to 27.4 percent in 2010/11. Besides the four major programs, many other programs developed during the last 20 years, predominantly serving rural communities. In fact, the aggregate coverage of these programs is higher than the coverage of Grameen Bank, serving

> 6 An _upazila_ is an administrative unit that is smaller than a district and consists of a number of villages.

10
# **Definitions and Sources of Variables Used in Regression Analysis**

|Variable|Definitions and Sources|
|---|---|
|∆%Deflator|Percentual Change of GDP deflator|
|∆%Deflator - ∆%CPI|Percentual Change of GDP deflator - Percentual Change of CPI|
|∆%Export (volume)|Percentual Change of the volume of the Export of goods and services as a share of<br>GDP; Source: World Development Indicators [WDI]|
|∆%GDP|Percentual Change of the Real Gross Domestic Product|
|∆%GDPpw|Percentual Change of the Real Gross Domestic Product Per Worker|
|∆%Import (volume)|Percentual Change of the volume of the Import of goods and services as a share of<br>GDP,Source: WDI|
|∆%CPI|Percentual Change of CPI|
|∆%M2|Percentual Change of M2|
|int2. Index|Change in the ratio of foreign assets by the Central Bank and M2|
|∆Log(ToT)|Terms of trade logarithm change|
|BK cycle (% change)|Percentual Change of the Baxter-King band pass cycle of Real Gross Domestic Product<br>Percentual Change of the Baxter-King band pass trend of Real Gross Domestic|
|BK trend (% change)|Product|
|Lagged dep. var.|Lag of the dependent variable|
|Financial account to GDP|Average Financial account to GDP; Source: IMF’s International Financial Statistics<br>[IFS]|
|Foreign_assets/M2|Central Bank foreign assets as percentage of M2 (money+quasi_money) ; Source: IFS|
|GDP|Real Gross Domestic Product; Source: WDI|
|GDP Deflator|GDP Current (LCU) / GDP constant (LCU), Source:WDI|
|GDP per worker|Log Real Gross Domestic Product Per Worker; Source: Penn World Table [PWT]|
|Nominal Gross domestic savings as % GDP|Nominal gross domestic savings as a share of GDP; Source: WDI|
|Real Gross capital formation as % GDP|Real Gross capital formation as a share of GDP; Source: WDI|
|HP cycle (% change)|Percentual Change of the Hodrick-Prescott cycle of Real Gross Domestic Product|
|HP trend (% change)|Percentual Change of the Hodrick-Prescott trend of Real Gross Domestic Product|
|CPI|Consumer Price Index, Source:WDI|
|Population|Total population; Source:WDI|
|Population growth|Population Growth|
|tcr|Annual average real exchange rate, calculate from montly data; Source: IFS|
|TCR|<br>Logarithm of tcr|
|Retribution to Labor as % Capital Compensation|Compensation of employees / Operating surplus, gross; Source: United Nations|
|ToT|Terms of trade, Exports as a capacity to import (constant Local Currency units).<br>Source: WDI|

33
<!-- Start of picture text --> IDEB Middle Schools & Single-Shift<br>Diff in Diff<br>wy<br>wi<br>3 ou<br>a<br>=)<br>wf<br>2) wn<br>3 +<br>no + foe So ;<br>eal “sy ag<br>= MOF * a“<br>wn ws ae”<br>or<br>2005 2007 2009 2011 2013 2015<br>Year<br>-------- Control group (288 schools)<br>——#— Became Single-Shift in 2011 (16 schools)<br>——®—_ Became Single-Shift in 2012 or 2013 (17 schools)<br><!-- End of picture text -->