rafmacalaba/data-use-mentions-extended huggingface dataset:
(4)|
|IDN exports per worker|-0.000143||-0.000134|-7.84e-06|-0.000156||-0.000153|-7.63e-05|
||(0.000327)||(0.000325)|(0.000333)|(0.000144)||(0.000144)|(0.000144)|
|Share of hhlds with computer||0.123|0.122|0.138||0.0452|0.0444|0.0536|
|||(0.0680)*|(0.0688)*|(0.0694)**||(0.0430)|(0.0433)|(0.0435)|
|IDN exports per worker *||||-0.00301||||-0.00183|
|Share of hhlds with computer||||(0.00104)***||||(0.000685)***|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.976|0.976|0.976|0.976|0.877|0.877|0.877|0.877|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population
in many parts of India, although these markets are still in their relative infancy in terms of product design and distribution.

Rainfall insurance is only one of a growing range of “microinsurance” products gaining popularity in the developing world. Other examples include policies relating to health, livestock, accidental death and disability, property, weather, and microenterprise risk. Lloyd’s (2009) estimates that around 135 million low-income individuals around the world already make use of microinsurance in some form and estimates a potential final market size of 1.5 billion to 3 billion households. Growth in these markets reflects a broadening of efforts toward greater financial access for the poor to include insurance and savings products in addition to microcredit.

A key challenge for microinsurance, as for other microfinance products, is to design it so as to minimize transaction costs and ameliorate incentive problems—factors which can otherwise make financial services to the poor prohibitively expensive. The key feature of rainfall index insurance that assists in this regard is that payouts are calculated on the basis of a _publicly observed_ and _exogenous_ variable: local rainfall. This significantly reduces transaction costs, because the household does not need to formally file a claim and the insurance company does not need to do an inspection to estimate the amount of loss. Since rainfall data are observed in close to real time, this also means that claims can in principle be calculated and disbursed quickly to affected households. The use of an index also greatly reduces incentive problems, because the household is unlikely to have significant private information about the distribution of future rainfall shocks and because the household cannot misreport the size of its loss.<sup>3</sup> The main disadvantage of index insurance is potential basis risk between the rainfall index and the actual income loss suffered by the household. This will be greater, for example, when the distance between the insured household and the rain gauge is larger or when actual
><br><br>|
||(9994)|(6923)|(8259)|(10442)<br>(10985)<br>(3998)|
|Lag11|.<br>31.481***|.<br>-13.012*|.<br>-|.<br>.<br>.<br>-<br>27.380**<br>-2.574|
||(9.786)|(6.819)|-|-<br>(10.891)<br>(4.550)|
|Constant|983.951***|983.141***|984.613***|984.149***<br>983.360***<br>985.420***|
||(1.969)|(2.022)|(2.026)|(1.955)<br>(1.966)<br>(3.223)|
|Observations|237,016|237,016|237,016|237,016<br>237,016<br>237,016|
|Number of pair<br>~~i~~d|42,619|42,619|42,619|42,619<br>42,619<br>42,619|
|R-squared|0.012|0.012|0.012|0.011<br>0.012<br>0.011|

Note: RoW is the Rest of the World. Estimation was conducted on the monthly trade data using a fixed effects estimator that accounts for cross-country-product heterogeneities. Robust standard errors are reported in parentheses. Asterisk indicate the level of *** ** * significance, _p <_ 0 _._ 01
...................... 10|
|Ap|pendix A: statistical tables ......................................................................................... 11|
|Ap|pendix B: SHIW empirical evidence on the value of use rights ................................ 17|
|Ref|erences ....................................................................................................................... 19|

_Usufructus est ius alienis rebus utendi fruendi, salva rerum substantia_ Roman law

* The World Bank, Development Data Group - Living Standards Measurement (DECLS Unit).

** Bank of Italy, Directorate General for Economics, Statistics and Research.
Garcia et al 2015),<sup>17</sup> so there may be little surplus wealth available to individual households. At the same time, estimates based on a referendum-style question conducted in the study’s 2016 survey (see Alix-Garcia, Phaneuf and Sims 2018) suggest that most participants would be willing to accept lower payments than they are currently receiving and still participate. Prior evaluations of PES in Mexico found that it likely had a small positive effect on assets of the 2003-2009 cohorts of the hydrological services program (with non-differentiated payments) and may have increased educational opportunities for some households (Alix-Garcia et al. 2015). Changes in program rules starting with the 2013 cohort mandated that more of the funds be spent directly on land management equipment or activities.

How this mandate affects household-level outcomes is theoretically ambiguous. This is because money does not necessarily flow directly to households from the pool of payment money. Ejido authorities, usually after discussion with the assembly, make decisions about how the money will be invested – this sometimes occurs through direct transfers to households, but more often it is through investment in public goods or by paying wages for forest work. If households receive wages for this work, as was the case in 65% of the ejidos surveyed in 2011 (Yañez-Pagans, 2015), then they may directly gain additional income compared to prior cohorts. However, if this compensation is less than their opportunity cost of labor, they could be worse off. In addition, the real value of the payments has eroded over time by 15-20%.

**Table 8** presents our results on multiple indicators of wealth. We observe no effects on household wealth, as measured by indices of housing characteristics, asset ownership and food consumption (Panel A). Regarding the detectable effects for these three wealth indices, they ranged between 0.10 and 0.12 (equivalent to 14-30% increases). Given the variation in the data, the sample would not have been able to detect these
the trajectory of impacts. Eight of the thirteen studies are very short-run, looking at impacts at one year or less after training. De Mel et al. (2012) find that the impacts differ in the short and medium term in their study. For example, examining impacts within the first year would show that business training for women out of the labor force led to large increases in business entry, whereas surveys 16 and 25 months after training show the control group had caught up in terms of business ownership rates.

# _Survey Attrition and Selective Survival or Start-up_

Survey attrition is another problem complicating inference, especially if the reasons for attrition are business failure, refusal because of disappointment with the training effects, or because successful business owners have moved out of the area. Survey attrition rates can be related to the timing of the follow-up surveys – a longer interval elapsing since baseline makes it more likely people have moved or that contact details have changed, but may also help reduce the chance that the control group attrits because they were disappointed not to be selected for training. Attrition rates range from as low as 5.3 percent in Field et al. (2010) and 6 to 8 percent in de Mel et al. (2012), up to 24 percent in Karlan and Valdivia (2011), 26% in Calderon et al. (2012), and 28 percent in Klinger and Schündeln (2011). It is perhaps no coincidence that the highest attrition rates come from three studies conducted in Latin America; our personal experience has been that in many Latin American countries firms are less willing to answer surveys than in Africa or South Asia, and transitions between wage work and self employment are frequent in the region (Fajnzylber et al, 2006).

10
# **The Important Role of Equivalence Scales: Household Size, Composition, and Poverty Dynamics in the Russian Federation**

Kseniya Abanokova, Hai-Anh H. Dang and Michael M. Lokshin<sup>*</sup> Key words: poverty, poverty dynamics, equivalence scale, Russia, panel survey JEL: I30, J10, O15

> * Abanokova (kabanokova@hse.ru) is junior research fellow with Higher School of Economics, National Research University, Russia; Dang (hdang@worldbank.org; corresponding author) is economist with the Analytics and Tools Unit, Development Data Group, World Bank and is also affiliated with IZA, Indiana University, and Vietam Academy of Social Sciences; Lokshin (mlokshin@worldbank.org) is lead economist with ECA Chief Economist’s Office, World Bank. We would like to thank Conchita D’Ambrosio, Sam Freije-Rodriguez, Jacques Silber, Rostislav Kapeliushnikov, Ambar Narayan, Sergey Roshchin, Ruslan Yemtsov, and participants at IARIW conference (Dresden), IARIW-HSE conference (Moscow), and seminars at the Laboratory for Labour Market Studies and the Centre for Labour Market Studies (Moscow) and the World Bank for useful feedback on earlier versions. We are grateful to Martin Biewen for sharing his Stata code. We would also like to thank the UK Department of International Development for additional funding assistance through its Strategic Research Program (SRP) and Knowledge for Change (KCP) programs. We also acknowledge support from the NRU-HSE Basic Research Program.
# **3. A Review of the Empirical Literature on Aid and Dependency**

The aim of this section is to look at the evidence that may indicate that aid programs can exert some distributional distortions that lead to changes in the labor supply. This is done for two reasons, firstly to show that there is a controversial debate on whether production unrelated cash flows (like pro-vulnerable income transfers, aid or remittances) tend to alter both individual and social decision making, and secondly in order to encourage more empirical research in verifying the theoretical statement developed in this piece of research.

The empirical literature studying the distortions generated by aid is definitely wide. The approaches to reporting this phenomenon are of three types: anecdotal, numerical and empirical. The former reports mainly case studies and views expressed in reports or media: they may be heavily influenced by the reporters’ opinion, though in most cases this represents the most powerful informative tool we can dispose of. The numerical one generally calibrates a model (i.e. computer general equilibrium) with parameters coming from some econometric estimation: it can provide relevant insights, but often leads to results largely dependent on the model’s assumption and analyzed transmission channels. The empirical one, developed through the house-hold survey data analysis can be very powerful, especially if it presents a credible identification strategy.

In support of the distribution distortion channel developed in this paper, some controversial pieces of evidence can be reported. For example, a report by Groupe URD (2005) on Afghanistan, states that some communities did modify their collective behaviors upon receiving external assistance (rising unemployment, public good maintenance suspension). The case of Northern Kenya in 2006 is also explicative: Thilo Thielke (2006) reported in _Der Spiegel_ that after a drought European food aid almost replaced the regional agricultural activity and that in the town of Loiyangalani and surrounding province more than 20,000 people were fed everyday free of charge over a population of roughly 40,000. Niger in 2005 is another case,
.2014.08.005.

Iftekhar, M.S., Islam, M.R. (2004) Degeneration of Bangladesh Sundarbans mangroves: a management issue. Int Forest Rev 6:123–135 Iftekhar, M.S., Saenger, P. (2008). Vegetation dynamics in the Bangladesh Sundarbans mangroves: a review of forest inventories. Wetlands Ecol Manage 16: 291–312.

IPCC (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability: Summary for Policymakers. Working Group II Contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report. IPCC, Geneva.

Islam, S.A., Bala, S.K., Hussain, M.A., Rahman, M.M. (2011). Performance of Coastal Structures during Cyclone Sidr. Natural Hazards Review 12(3).

Islam, S.A., Miah, S.A.Q., Habib, M.A., Alam, M.M. (2016). Growth and yield of Sonneratia apetala (keora) plantations raised from different seed sources in the central coastal belt of Bangladesh. Journal of Bioscience and Agriculture Research 6.

29
# **5.1 Should researchers use a penalty parameter other than the plug-in default?**

As discussed above, the “plug-in” penalty parameter behaves well asymptotically, but W ̈uthrich and Zhu (2023) show that it can underselect variables in finite samples when the number of control variables in reasonably large. The alternative is cross-validation. Chernozhukov et al. (2024) suggest that cross-validation instead raises the risk of overfitting, and can perform poorly in moderately sized samples as a result. We examine how cross-validation performs in practice using both our replication data and simulations.

Figure 5 shows simulation results, varying the number of observations between 100 and 10 _,_ 000.<sup>12</sup> Panels (a) and (b) show that the cross-validation method tends to select far more variables compared to the plug-in approach, for both steps 1 and 2. For example, when _n_ = 500 the median number of variables selected that are predictive of the dependent variable using the plug-in penalty parameter is one, compared to five for cross-validation. Since the oracle estimator includes five control variables, this suggest that there might be some power advantage of using cross-validation to select the penalty parameter in smaller samples. However, panels (a) and (b) also show that cross-validation often selects too many variables. The method is also unstable, with a high variation in the number of variables selected. This could reduce precision, due to reasons discussed above. Overall, panel (c) shows that the median standard error tends to be smaller for cross-validation in smaller samples, and equal to the plug-in parameter in very large samples. However, the variation in standard errors is larger, especially in smaller samples. That is, using cross-validation risks overfitting.<sup>13</sup> Table 3 examines the performance of cross-validation in real field experiments by re-estimating each treatment effect using cross-validation instead of the plug-in penalty parameter. Comparing the number of variables selected to those presented in Table 1
male-female wages, and found that, when controlling for educational attainment, the mean wage gap is about 8 percent. The mean wage gap reduces marginally when cognitive and noncognitive skills are added to the model. Examining the wage gaps at different points of the wage distribution, they found that the gap in Bangladesh is wider at the bottom end of the distribution and that cognitive and noncognitive skills explain about 88 percent of the gap at the upper end of the wage distribution and about 69 percent at the bottom of the distribution.

Mueller and Plug (2006) and Fortin (2008) provide evidence for the United States. Mueller and Plug used data from the Wisconsin Longitudinal Study, and although they found significantly strong associations between their measure of cognitive skills (IQ) and earnings (especially for men), they found no gender differences in responses to cognitive skills. For noncognitive skills, they report that about 3 percent of the gender gap is explained by differences in, and returns to, personality traits. Fortin’s study examined the effect of noncognitive skills—self-esteem, locus of control, the importance of money/work, and the importance of people/family—using data from two 6
Study databases covering primarily developed countries (LIS). Household income data are organized in “spells,” i.e. income changes between two survey years, calculated as average annual log differences.<sup>8</sup> These are calculated for average income, income at the bottom 20 percent and the bottom 40 percent. We focus on those non-overlapping spells that are at least five years long, which provides 299 spells for 117 countries with a median spell length of 6 years, which is the preferred sample of Dollar et al. (2016).

# **_Control Variables_**

Building on the existing literature, our model also includes two sets of control variables that serve as proxies for a variety of policies and institutions that might matter for growth and be relevant for changes in the incomes of the poorest segments of the population (a complete list with sources and descriptive statistics is given in Appendix A). The first group includes variables capturing policies that may matter for the distribution of income:

- The mean income growth rate to take into account the strong link between mean income growth and income growth at lower parts of the distribution (Dollar and Kraay, 2002);

- Inflation and inflation variation as two measures to control for the macroeconomic environment, reflecting the idea that a stable macroeconomic environment is important for economic agents to plan ahead and thus a relevant enabling factor for income generation, especially when access to financial insurance is limited, such as for the poor (e.g. Easterly and Fisher, 2001);

- Telecommunication connectivity (use of land and mobile lines) as a proxy for infrastructure, which has been shown to be an important driver of growth and income generation, especially for low-income countries (Calderon and Chong, 2004; Araujo et al., 2014; Moller and Wacker, 2015);<sup>9</sup>

- School enrollment and life expectancy (alternatively child mortality) are included as standard variables in most growth regressions, as they reflect
# **3. Data**

The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS).<sup>4</sup> The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015 - were conducted using mobile phones.<sup>5</sup> The original sample comprised 501 respondents (51% Male, 49% Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako,<sup>6</sup> refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2% per wave.

We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location. It should also be noted that the respondent was selected randomly among household members above the age of 18 (split between men and women) to obtain a good representation of the (subjective) opinions of male and female adults. As a consequence, and unlike more typical welfare surveys, the respondent often was not the male household head or spouse. Additional information on the data methodology can be found in (EtangNdip, Hoogeveen, & Lendorfer, 2015).

> 4 All data from this survey can be downloaded from <u>http://www.gisse.org/pages/enquete-continue-sur-les-deplacesrefugies/enquete-sur-les-deplaces-et-les-refusgies.html</u> and from: <u>http://
level. For example, Abdulkadiroglu et al. (2011) show that charter schools in Boston with binding lotteries generate larger gains in students’ standardized test scores. Khattri et al. (2013) show that the introduction of SBM has a positive impact on the school-level test scores in 23 school districts in the Philippines, and Hanushek et al. (2013) show with over one million student observations for 42 countries using the PISA standardized test that SBM reforms in well-developed institutions may be conducive to student achievement, while in low-performing institutions the opposite effect will occur.

Improving school performance, especially in poor communities, remains a challenge facing most countries. One policy being examined by many developing countries is SBM; Hong Kong SAR, China; Indonesia; Kenya; Kyrgyz Republic; Nepal; Paraguay; and Mexico are among the main countries implementing the program. To date, the empirical evidence on the effects of SBM interventions on student academic performance in developing countries is quite limited. Several studies rely on cross-sectional variation, ex-post propensity score matching and exclusion restrictions – either using functional forms or weak instrumental variables – thus leaving their ability to establish causality open to question (see, for example, the works of Jimenez and Sawada 1999, 2003 on El Salvador’s EDUCO; DiGropello and Marshall 2005 on the effects of the Honduras PROHECO program; King and Ozler 1998, King et al. 1999 and Parker 2005 on Nicaragua’s Autonomous School program; or López-Calva and Espinosa 2006 on the impacts of AGE as well as the other Compensatory Program supports on test scores). Notable quasiexperimental exceptions include Shapiro and Skoufias (2005) and Murnane et al. (2006) who use difference-in-differences models to estimate the impact of the PEC national intervention on drop out, repetition and failure rates. Gertler et al. (2008a) combine quantitative (difference-indifferences) and qualitative methods to examine the effects of an initiative that involves parents directly in the management of school grants transferred to the parent associations in schools in highly disadvantaged rural communities.

Duflo