rafmacalaba/data-use-mentions-extended huggingface dataset:
Despite their intuitive appeal, transfer schemes that target poor communities remain difficult to design. One of the difficulties concerns information on the spatial distribution of poverty. In many developing countries there is now a good deal of experience with methods to conceptualize, measure, and analyze poverty. Yet most of the empirical evidence that has been brought to bear on this topic tends to be at the national level. This is primarily due to the fact that data on incomes or consumption expenditures, which serve as prime input into the quantitative analysis of poverty, tend to derive from sample surveys of households. The sample size of such surveys is often rather small. This implies that statements about the spatial distribution of poverty can generally refer only to very broad geographic breakdowns. It is not uncommon, for example, for surveys to be “representative” only for urban and rural areas of a country, in combination with perhaps a very crude geographical breakdown of the country into broad regions (north/south or hills/plains). While there is a general suspicion in many countries that poverty occurs in geographic “pockets” that are defined fairly precisely, sample surveys do not readily allow one to confirm or refute such notions.<sup>2</sup> Absent detailed information on poverty outcomes at the local level, policymakers interested in targeting transfers to the local level have often sought to use proxy indicators of local poverty outcomes. For example, the Microregions program that is being contemplated by SEDESOL (Ministry of Social Development) in Mexico is proposing to deliver social services and to promote productive investment projects in a set of municipalities that have been selected on the basis of a marginality index produced at the municipality level by CONAPO (de Janvry, Sadoulet, Calva and Solaga, 2003). This index is based on a set of simple variables taken from the population census, and is believed to be correlated with conventional notions of poverty.

> 2 However, recent research shows that beliefs regarding the existence of  such “pockets of homogeneous poverty
the foreign sector in each location, the proportion of workers employed by foreign firms relative to total workers is computed from the _Enterprise Survey_ data which cover all registered enterprises. Second, the study differentiates how the determinants of migration vary depending on job outcomes defined by ownership and destination. Finally, whereas the previous studies using the micro data usually investigated characteristics either in sending or receiving areas,<sup>6</sup> this paper employs a two-stage approach investigating characteristics of both areas. In the first stage, a multinomial logit model is employed to determine how the characteristics in source areas and personal attributes “push” individuals to move out (out-migration). In the second stage, conditional logit models are estimated to investigate how destination-specific characteristics “pull” different types of migrants (in-migration), given the migration that has actually taken place.

Following this introduction, Section II presents an overview of the trends and context of internal migration in Vietnam. Section III reviews related studies in the current literature. Section IV describes data sources and descriptive statistics. Section V uses a multinomial logit model to investigate the determinants of the out-migration decision. Section VI implements conditional logit models to explore the determinants of destination choices among migrants. Section VII presents concluding remarks.

# **II. Background**

Following the country’s reunification in 1975, the Government of Vietnam implemented an extensive national population and labor relocation policy (Dang, Goldstein and McNally, 1997; Dang, Tacoli and Hoang, 2003). _Inter alia_ , the government organized resettlement programs with the establishment of the so-called “New Economic Zones”. In order to redress imbalances in

> 6 One exception is Liang and White’s (1997) study which estimates both in-migration and out-migration models in China.
The production system is overwhelmingly rainfed, characterized by limited access to irrigation and diminishing average land holding sizes due to population pressures. The rainfall is unimodal, and maize is the main staple crop, grown by nearly 100 percent of the farming household population.<sup>15</sup> Over the last two decades, agricultural productivity, as measured by maize yields (kilogram/hectare), has been erratic, as shown in Figure 1. The factors that are commonly cited as underlying the agricultural productivity trend include weather variability, declining soil fertility, limited use of improved agricultural technologies and sustainable land management practices, rationed agricultural extension services, market failures, and underdeveloped and poorly maintained infrastructure (World Bank, 2007). The majority of the farming households

> 13 Unless otherwise stated, the statistics reported in Section 3.1 originate from data.worldbank.org/country/Malawi. 14 The GDP contribution of agriculture is for 2011. The estimate of the percentage of Malawian households owning and/or cultivating land is based on the Third Integrated Household Survey (IHS3) data. 15 The estimate is based on the IHS3 data.

7
A third set of assumptions are related to the effectiveness of mitigation _(m)_ strategies. We assume that remote learning is never as effective as classroom instruction. It is hard to keep children engaged cognitively with all the distractions in the household, devices have to be shared between siblings, and it can be hard for families to decipher television programming. Moreover, access to a television or internet (the main channels of delivering remote learning) is highly unequal. We also assume that the economic shock that families are experiencing will also have detrimental effects on the ability of children to make effective use of any available mitigating strategies, especially as family incomes drop, family and child food security worsen, and household stress increases.

In our simulation, we bring together three elements, the government supply (or expected coverage) of alternative education modalities (G), the ability of households to access (or take-up) these alternative modalities (A), and the effectiveness of the alternative modalities (E). Building on existing household surveys, such as the MICS, DHS, and other multitopic household surveys, we were able to identify the share of households with access to internet, computer, mobile phones, land lines, radio, and television (Table 2). This information helped us shape some of our main scenarios. We assumed that all governments (G) were offering some type of alternative modality, but household access (A) and the effectiveness (E) of these modalities were heterogenous depending on the income of the country.

**Table 2: Household access to technology**

||||**Sh**|**are of house**|**holds possess**|**ing:**<br>||
|---|---|---|---|---|---|---|---|
|||**mobile**||
we recover the causal impact of Fonden on local economic activity by rescaling the coefficient derived in the first step by the coefficient derived in the second step.

We measure changes in economic activity at the municipal level using high resolution satellite imagery that allows us to measure the intensity of light as observed from outer-space. Because our night light measure is a good proxy of subnational level economic activity we are able to track the differential economic performance created by the rapid provision of reconstruction funds.

Our main finding is that municipalities with access to Fonden grew between 2 to 4% more than those without Fonden in the year following the disaster. Conservative benefit cost ratios, for the same time period, are in the 1.52 to 2.89 range. The paper additionally documents the impact of Fonden over the post disaster period. We find that the economic expansion triggered by the program peaks 15 months after the disaster, and that from this point onward municipalities without access to Fonden begin to catch-up. Consistent with the previous findings, the paper additionally documents a large and sustained increase in the growth of construction employment

> 1A municipality is a second level administrative unit in Mexico. There are currently 2461 municipalities.

2
The current paper followed the comprehensive approach about national innovation systems presented at Cirera and Maloney (2017) and shed light on key innovation patterns and constraints within a selected group of developing East Asian countries (Cambodia, China, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Thailand and Vietnam). The paper presented two sets of empirical exercises drawing from different data sets, at the country and firm levels. Some illustrative results are noteworthy.

_First_ , a simple country benchmarking exercise, drawing from the GII data set, was conducted to identify which innovation enabling factors matter the most to explain innovation output performance across countries. The following developing East Asian countries were covered in the data set: Cambodia, China, Indonesia, Malaysia, the Philippines, Thailand and Vietnam. Results suggest that horizontal factors that in principle impact overall firm performance and accumulation of any type of capital (not only knowledge capital) also matter as determinants of innovation performance: almost 35 percent of variation of innovation output performance across all countries covered by the GII data set is explained by the _Institutions_ , _Infrastructure_ and _Market Sophistication_ pillars. Among these enabling factors, the one where the developing East Asian countries covered by the data set mostly underperform is _Institutions_ ; precisely in terms of regulatory environment and business environment. These results reinforce the idea presented in Cirera and Maloney (2017) that complementary factors of the business environment - that go beyond specific barriers to innovation - are critical to improve the performance and returns to innovation. Therefore, boosting the regulatory environment and business environment is key to improve the innovation output performance of this selected set of countries.

21
enterprises in our survey sample. The results for male managers are quite similar to the pooled data for nonfarm analysis. Financial services, transportation, electricity, and facing payment disputes are positively associated with enterprise performance. In addition to all the above factors, distance to markets is positively correlated with performance, and management innovation is negatively correlated. As for management innovation, a negative sign can be interpreted as the entrepreneurs with better performance having not even considered improvement or innovation, but entrepreneurs with worse performance had to improve or innovate for survival, but all by their own ideas and sources. Receiving business services is not statistically significant. The availability of business services is extremely limited in rural areas. According to the RIC survey, marketing services are available in 20 percent of the communities, and business services are available in 25 percent of the communities. However, the utilization of business services is even worse. Only 1.0 percent of farm entrepreneurs and 2.5 percent of nonfarm entrepreneurs used management-related services, such as consulting, marketing, and accounting, during the past year.

# **4.3 Rural Labor Migration**

Migration, more move-in than move-out, may reflect an increase in economic activities in the community. The survey data on migration are shown in Table 4-3.

**Table 4.3 Migration (% communities)**

|Governorate|Sana’a|Lahj|Dhamar|Total|
|---|---|---|---|---|
|More Move-in|50.0|16.7|16.7|27.8|
|More Move-out|33.3|66.7|16.7|38.9|
|The same move-in & move-out|0|8.3|50.0|19.4|
|No move-in & move-out|16.7|8.3|16.7|13.9|

_Source:_ 2010 Yemen RIC Survey

# *
nal of child deprivation — that may usefully, when combined with remote-sensed data, inform the design of social protection and other government assistance. We do not, however, take a position regarding the precise nature of the support required to alleviate the underlying deprivation(s). Further, a weather disturbance in an agrarian context may be thought of as an additional deprivation (in the sense used by Sen (1999)), because it constrains choices and livelihood opportunities. Support triggered via an adaptive safety net should therefore not be limited to maternal and child health, but instead involve sectors most relevant to that location and country.

# **2.2 Measuring Shocks to Crop Production**

There is no consensus on the best approach to monitoring crop production in Sub-Saharan Africa. At the same time, there is broad agreement on both the challenges and the relevant sources of information. There are two related challenges to measuring crop production in these data-constrained contexts (e.g. Burke and Lobell (2017), Jin et al. (2017)). First, there is a dearth of information on actual crop yields, particularly for small-holder farmers — farmers that constitute the majority of poor households. Second, without adequate ground-truthing, analyses of high-resolution satellite data provide noisy predictions. This is exacerbated by the fact that areas under crop production are constantly changing and the mix of crops produced together is hard to classify across contexts. Consequently, it is also challenging to transfer knowledge gained in a particular context to other locations.

Therefore, organizations charged with monitoring crop production in Sub-Saharan Africa rely on remote-sensed information averaged at broader spatial scales (e.g. Funk et al. (2019)) — traditionally focusing on vegetation (e.g. Becker-Reshef et al. (2010), Brown (2014)) and rainfall (Funk et al. (2015)), both of which have longer time series than the newer measures. Unlike rainfall, the Normalized Difference Vegetation Index (Tucker (1979))
93)|(0.23)|(1.35)|
|Political stability *|0.176*|-0.0551|-0.232|
|NCE dummy|(1.77)|(-0.42)|(-1.59)|
|Time trend|0.0184|0.316***|0.279***|
|(=0 in 2000)|(0.74)|(7.09)|(5.94)|
|n|1,378|1,392|1,378|
|R-squared|0.136|0.300|0.232|
|RMSE|4.74|8.03|7.97|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. The dummy for being a new commodity exporter is equal to one in the first five years of fuel exceeding 25 percent 12
by economists to measure wellbeing). We present the model using a case study – the 2005 floods in Mumbai, India, for which macro‐ and micro‐economic data are available. We then use the model to define and assess the socioeconomic resilience of 90 countries to river floods, identify policy priorities to reduce the impact of floods on wellbeing, and help design holistic risk management strategies tailored to each country.

An Online Technical Note<sup>1</sup> provides all the equations and technical details of the model, sensitivity analysis to show the robustness of the approach, and a comparison with other indicators for resilience or vulnerability. The model and the data are also available online.<sup>2</sup> We propose a new quantifiable definition of _socioeconomic resilience_ , the ratio of _asset_ losses to _welfare_ losses<sup>3</sup> : Socioeconomic resilience �<sup>Asset losses</sup> Welfare losses

> 1 The online technical note is available at

<u>http://pubdocs.worldbank.org/en/362361462825935820/pdf/Online‐Technical‐Note‐submit.pdf</u> 2 The code and data set are available at github.com/adrivsh/resilience_indicator_public/

> 3 This definition combines the previously proposed notions of macroeconomic and microeconomic resilience (Hallegatte, 2014).

2
, Commerce and, Construction or civil engineering. These sectors engage a larger share of those employed in the Centre and north east regions and 47 percent from the poorest decile. Secondly, the pandemic will negatively affect those employed informally which is widespread and the highest in construction (68 percent), commerce (51 percent), tourism (45 percent) and textiles (43 percent) sectors. Third, jobs which cannot be done from home, including agriculture and industries, face more uncertainty. Last, those who spend more on food will be affected by price shocks, especially the poor for whom food is a major share of their consumption expenditure.

Our analysis combines the labor shock and price shock induced by COVID-19 simultaneously and simulates post-pandemic consumption. Our estimates indicate that poverty is expected to increase by 50 percent under Scenario 1 (optimistic), and to almost double from 13.7 to 25.9 percent under Scenario 2 (pessimistic). At the same time, inequality is expected to increase slightly due to the COVID-19 outbreak in Tunisia. In fact, households with per capita consumption in the poorest 20 percent of the distribution are simulated to be hit the hardest.

Using the post-crisis welfare distribution, this analysis also helps identify the individuals who are expected to fall into poverty due to COVID-19. Individuals who are expected to fall into poverty due to COVID-19 are likely to be disproportionately resident in the Center west and South east regions and are also more likely to be women, stay in larger sized households, be employed with no contract, and have no access to health coverage.

These findings show that, going forward, it would be extremely important to ensure that economic growth benefits the poor and thus be inclusive. Fostering jobs as well as alternative livelihood mechanisms in the 18
,150 villages in the ten UBR districts. The census extract also serves two main purposes. First, it is used, along with the parameters of the census model estimated in the household survey, to generate the benchmark welfare used for evaluation. Second, it provides a randomly selected sample of villages that is used to simulate a partial social registry, as explained in the methodology section below.

## Survey data

The analysis uses the fourth Integrated Household Survey (IHS) of 2016, which is made publicly available through the World Bank’s Living Standard Measurement Survey (LSMS) program. The survey includes a cross-sectional sample of 12,447 households surveyed in 779 enumeration areas (EAs). Thus, there are roughly 16 sample households per enumeration area. It is considered to be representative at the district level. Because it is an LSMS survey, jittered enumeration area coordinates are also publicly available.<sup>12</sup> The IHS is used in the analysis for two primary purposes. The first is to estimate a model that predicts per capita consumption as a function of household variables common to the survey and the census extract, as a basis for constructing the benchmark measure of welfare in the census. Besides serving as a benchmark for evaluation, this predicted welfare measure is also used to simulate a partial registry in a subsample of villages. The second

> 11 See Lindert et al., 2018, for more information on the UBR.

> 12 Van der Weide et al (2022) finds that the jittering reduces the correlation between census and geospatial-based estimates for traditional authorities in Malawi by a modest amount.

7
. For brevity, only the education results are presented in Appendix Table 2.

_Age Impacts._ One-third of our sample is below the age of 15. To check whether this recall effect is driven by high morbidity and differential recall for sickness among children, we restricted the sample to those above the age of 15 and find no difference in the estimated parameters. The estimates in the observational data for number of acute illnesses, the probability of a single acute illness, the labor impacts of acute illnesses and doctor visits are respectively 0.55 (0.57), 0.13 (0.12), 0.024 (0.029) and 0.13 (0.14) in the full and adult-only samples, the latter in parenthesis. For the labor impacts of acute illnesses where the differences are somewhat larger, the interaction between adults and the recall impact is not significant (p-value = 0.859).

_Respondent Impacts_ . Every effort was made to retain the same primary respondent in all surveys, weekly and monthly. We code an indicator variable for whether the respondent was the designated primary respondent to check for respondent bias. In the case of the weekly surveys, this is aggregated up to the percent of weeks in the relevant month that the respondent was the designated primary respondent. The data suggest that 16
Our analysis provides novel contributions to two established literatures: on household responses to aggregate shocks, and on women’s empowerment.

On household responses to shocks, a vast literature studies _ex ante_ and _ex post_ mechanisms to mitigate idiosyncratic risks [Townsend 1994]. A smaller literature examines responses to aggregate shocks, given greater identification challenges. Much of this has focused on financial crises [Fallon and Lucas 2002, McKenzie 2003], with a smaller literature studying shocks caused by epidemics or viral transmissions [Lee and McKibbin 2004, Adda 2016].<sup>6</sup> In the presence of aggregate shocks, coping strategies for dealing with idiosyncratic risk (such as temporary migration), often break down. Moreover, in aggregate crisis when the marginal utility of consumption is very high, households might be forced to engage in behaviors that do not maximize their long run welfare or permanent income, such as pulling children out of school [Jacoby and Skoufias 1997. Thomas _et al._ 2004, Ferreira and Schady 2009], or engaging in transactional sex [Dupas and Robinson 2012].<sup>7</sup> Our analysis also contributes to work on women’s empowerment. It has long been recognized that economic development and women’s empowerment are closely linked [Doepke _et al._ 2012, Duflo 2012, Jayachandran 2015], and that core dimensions of disadvantage stem from women having limited agency over their bodies, facing barriers to investing in their human capital, and having poor labor market prospects [Field and Ambrus 2008, Dupas 2011, Jensen 2012, Kleven and Landais 2017]. Most importantly, hard-earned gains in women’s empowerment can be quickly erased by aggregate economic shocks, and it is in times when households face the greatest crises, that gender differentials in outcomes are most likely to open up [Duflo 2012]. There is a long established literature from across disciplines discussing the issue using observational data [Goldin and Katz 2002, Doepke _et al._ 2012,