rafmacalaba/data-use-mentions-extended huggingface dataset:
migration support this conclusion. From a within-country perspective, Hirvonen (2016) shows that temperature anomalies decrease (male) migration in rural Tanzania because of tightened liquidity constraints. Liu et al. (2022) study responses to slow-onset temperature changes in Indian districts, and conclude that progressive warming and rising temperatures inhibit structural transformation and limit rural-urban migration for households living in isolated areas. Other authors emphasize that, in most cases. migration depends much more on political and economic factors and is only minimally associated with slow-onset changes. For example, Selby et al. (2017) find that environmental variables have a marginal role in explaining migration flows to the Syrian Arab Republic in the period before the outbreak of the civil war. Similarly, Niva et al. (2021) performed a geospatial analysis of a gridded global net migration data set for the decade 1990-2000, and found that slow-onset changes, such as droughts and water scarcity, were the dominant environmental events in explaining net-migration. Yet, they also emphasize that income levels and adaptive capacity crucially mediate environmental variables in determining migration outcomes.

In short, while there seems to be a prevailing consensus that slow-onset shocks do increase migratory flows, there are cases in which the opposite happens, and affected populations are instead trapped in 6
# **Table C4.1: Balance tests for matching**

||Me<br>|an<br>|||
|---|---|---|---|---|
||Treated|Control|t-stat|p>|t||
|Pa|nel A: Ba|lance|||
|Log age|2.28|2.16|1.65|0.09|
|Foreign ownership|12.77|11.34|0.69|0.49|
|State ownership|5.70|4.71|0.59|0.55|
|Importer|0.56|0.56|-0.13|0.90|
|Exporter|0.39|0.41|-0.51|0.61|
|Panel B:|Balance (|Alternativ|e)||
|Log age|2.37|2.36|0.07|0.95|
|Foreign ownership|13.37|11.71|0.77|0.44|
|State ownership|5.97|5.66|0.17|0.86|
|Importer|0.57|0.55|0.55|0.59|
|Exporter|0.40|0.42|-0.43|0.66|

_Note_ : This table presents balance tests for propensity score matching, using the 5 nearest neighbors, with replacement. Suharto family firms are matched with non-connected firms, based on the following variables: logarithm of firm age, foreign and state ownership, indicators for being an exporter and importer, industry and year fixed effects; and the sample is restricted to 19931996. In panel A the matching is done on all firms, while in panel B only on firms that exist since 1993. After obtaining the frequency (
# **Annex 2: The synthetic panel methodology and its application to Nepal**

A proper study of economic mobility that is being proposed has fairly demanding data requirements. At the minimum, it is necessary to be able to observe the same household in at least two time periods and the richer the amount of information available on the household, the richer the analysis that can be done. These “panel data sets” as they are called are fairly hard to come by in many developing countries. In the case of Nepal, the three rounds of NLSS that have been completed have an integrated panel component, i.e., a subset of the sampled households have been tracked over time, but due to sample attrition, it has been found that the surviving households are no longer representative of the nation as a whole.

As the name suggests, the proposed methodology overcomes these shortcomings and builds on an imputation based methodology to construct a “synthetic panel” that makes it seem like the same households were observed in two time periods. The approach relies fundamentally on time-invariant individual and household characteristics. Specifically, it entails predicting consumption of households surveyed in time _t_ for period _t-k_ using the estimated “returns” to these time-invariant characteristics in period _t-k_ . The comparison of “predicted” consumption in _t-k_ with the actual consumption in period _t_ forms the basis of the analysis of mobility, particularly movement in and out of poverty.

To write more formally, consider two rounds of cross sectional surveys (denoted as rounds 1 and 2) and let �� and ��be the corresponding consumption for the two rounds (these are consumption for household _i_ , but we suppress those subscripts). For a given poverty line, say, _z_ , we are interested in estimating (a) the fraction of poor households in the first round who escaped poverty (Pr(�� ��| �� ���) or remained in poverty in the second round, (Pr(�� ��| �
does not escalate into violence. Within this spectrum falls a range of local conflicts that may persevere and/or escalate to major instances of violent conflict or even civil war.

# **_2.1. Defining and Measuring Local Conflict_**

Measuring conflict incidence raises two fundamental issues: (a) the exact definition of conflict and the threshold at which an incident is defined as violent conflict within a given geographic jurisdiction; and (b) the conditions under which respondents might misreport or misperceive instances of conflict. Reporting biases relating to conflict are of special concern for statistical evaluations if these are themselves associated with some of the factors posited to be associated with conflict.

The Indonesia statistical agency (BPS) has adopted a definition of local conflict beyond some threshold of violence within a given locality in the past year, that may have been associated with loss of life, serious injury, or property damage. Localities are rural villages ( _desa_ ) or their urban equivalents ( _kelurahan_ ). Responses are compiled from village leaders and central government statistical agents ( _mantri statistik_ ) posted at the sub-district level. This large-scale, quantitative collection of conflict data has its limitations. Given the challenges of capturing local conflict as a discreet incident, our paper explicitly integrates an in-depth qualitative conflict mapping exercise conducted in eight sub-districts (Barron _, et al._ 2004a), as well as comparisons with other quantitative data. We argue that the complexity of conflict, matched by its often widespread incidence, requires mixed quantitative-qualitative approaches (cf Rao and Woolcock 5
. Residual policy variation should be driven by prediction errors by states in setting the SUA since most of them use lagged data, as well as idiosyncratic state-level differences in methodologies used to set the SUA. We show that results are nearly identical for states using either SUA methodology in Table N5. Since we can fully observe how states that use lagged CPI are setting the SUA and we control for contemporaneous energy prices and quantities consumed, the fact that states that recalculate the SUA using utility data also have similar price responses suggests that any unobservable variation in the SUA does not pose significant endogeneity concerns.

# **C.2 Other Adjustments to the SNAP Formula Made by Individual States**

Second, states have the flexibility to relax the federal eligibility rules through a policy called Broad-Based Categorical Eligibility (BBCE).<sup>59</sup> For instance, in the case of the gross income limit in 2015, 11 states followed the federal 130% limit, whereas 14 states extended the limit to 200%, with other states choosing a limit in between. In the case of the asset limit, 24 states waive it altogether; 11 states waive it for most households, but if a household cares for an elderly or a disabled member and the household’s gross income exceeds 200% of the federal poverty line, the states apply an asset limit of $ 3,250; New Hampshire waives the asset limit for households with children; New York waives it for households with dependents or households with earned income. All states except four allow for at least one vehicle per household to be excluded from countable assets. This implies that heterogeneity exists in the composition of SNAP participants across states.

59BBCE allows states to equate their SNAP eligibility rules with TANF eligibility rules, over which states have some jurisdiction.

51
Croson, R., R. Gneezy. 2009. “Gender differences in preferences.” _J. of Econ. Lit._ 47(2), 448-474.

- Cull, R., L.C. Xu. 2000. “Bureaucrats, State Banks, and the Efficiency of Credit Allocation: The Experience of Chinese State-Owned Enterprises,” _J. of Comparative Economics_ 28, 1-31.

- Cull, R., L.C. Xu. 2003. “Who Gets Credit? The Behavior of Bureaucrats and State Banks in Allocating Credit to Chinese SOEs,” _Journal of Development Economics_ 71, 533-559.

- Cull, Robert and Lixin Colin Xu. 2005. "Institutions, Ownership, and Finance: The Determinants of Profit Reinvestment among Chinese Firms." _Journal of Financial Economics_ , 77(1), 117-46.

- Cull, Robert, Lixin Colin Xu, Tian Zhu.  2009. "Formal Finance and Trade Credit During China's Transition," _Journal of Financial Intermediation_ 18(2), 173-192.

- Cull, R., W. Li, B. Sun, L.C. Xu. 2015a. “Banks, the Government, and Financial Constraint: Evidence from a Large Sample of Chinese Firms,” _J. of Corporate Finance,_ 32, 271-294.

- Cull, R.; L. Gan, N. Gao, L.C. Xu. 2019. Dual Credit Markets and Household Usage to Finance: Evidence from a Representative Chinese Household Survey.” _Oxford Bulletin of Economics and Statistics_ 81(6), 1280-1317.

- Cull, Robert; Lixin Colin Xu; Xi Yang; Li-An Zhou and Tian Zhu. 2017. "Market Facilitation by Local Government and Firm Efficiency: Evidence from China." _J. of Corporate Finance_ , 42, 460-480.

- Dethier, J.J.; M. Hirn, S. Straub. 2010. "Explaining Enterprise Performance in Developing Countries with Business Climate Survey Data." _World Bank Research
Figure 35: State Level Water Deprivation Shelter Deprivation - by State - MICS 80% 70% 60% 50% 40% 30% 20% 10% 0% Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimates are for children aged 0- 17.

Figure 36: State Level Shelter Deprivation a) b) <!-- Start of picture text --> a) b) c)<br>Shelter Deprivation - by State in Shelter Deprivation - by State in Shelter Deprivation - by State in<br>North Central - MICS North East - MICS North West- MICS<br>80%<br>80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10%<br>10%<br>0% 0%<br>0% Children 0-17 Children 0-17<br>Children 0-17<br>Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Benue FCT Abuja Kogi Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>Kwara Nasarawa Niger<br>d) e) f)<br>Shelter Deprivation - by State in Shelter Deprivation - by State in Shelter Deprivation - by State in<br>South East - MICS South South MICS South West- MICS<br>80% 80%<br>80%<br>70% 70%<br>70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0
communities that are much harder to access, resulting in higher non-response rates through noncontact, although they do not provide evidence for this (reasonable) assertion.

Kennickel (2019) shows that non-response rates in the US Survey of Consumer Finances are much higher for those at the very top of the income distribution. The survey has two sample frames, one derived using a regular household survey frame and another for rich households, which is derived from tax filer data. For the richest stratum of the tax filer derived sample frame, described as “a group in the upper reaches of the top 1%”, the response rate in 2013 was less than 10%, compared to 66% in the sample obtained from the regular household sample frame (Kennickel, 2019: 446).

Hlasny and Verme (2018) used data from the Egyptian Income and Expenditure Survey to show that unit non-response rates are higher in Egyptian governorates with higher mean income per capita. The National Income Dynamics Study (NIDS) in South Africa obtained much higher unit non-response rates than the previously mentioned LMIC surveys- 31%. Non-response rates by race of the predominant racial group in the primary sampling unit (PSU) also varied. Predominantly white areas had nonresponse rates of 64% while predominantly black areas had a non-response rate of 24% (Leibbrandt et al. 2009). Since race is strongly positively correlated with income in South Africa, the implication is that non-response rates are increasing with income, which is a concern when measuring the top of the income distribution.

# _Item non-response_

We have noted that refusals or non-contacts may limit how well one measures the top of the income distribution if the rich have high non-response rates. But those individuals who do respond to a survey may not answer specific questions about incomes, wealth or consumption, since these are more sensitive. This issue is called item non-response. Like unit non-response, this can be missing completely at random, or it may be related to other individual characteristics or income levels themselves. If the
housewives, even if helping in a family business or farm (Franck and Olsson 2014; Muller and Sousa 2020).

Activities lists ask respondents if they engaged in specific, often under-detected local activities (e.g. making cheese, butter, or ghee to sell) (Anker and Anker 1989; Langsten and Salem 2008). Past research from the Arab Republic of Egypt has shown the superiority of the individual activities list in capturing work, compared to keyword detection approaches (Langsten and Salem 2008). Recovery questions take a similar approach, asking if the individual was working on a family farm, for example, and whether the products were intended for market (Discenza et al. 2021; Durazo et al. 2021). These innovations can substantially increase estimates of women’s employment, for instance finding 22% more employed women (8.1 percentage points higher employment to population ratio) in Sri Lanka (Discenza et al. 2021). Similarly, Anker and Anker (1989) have shown that estimates of female participation in the market labor force in Egypt can vary from 15% to 34% depending on what questions are asked and whether it is the respondent herself who answers or a proxy.

Innovations to date in detecting women’s employment have focused primarily on improving individual-level questions and the training of enumerators, for instance emphasizing the need to interview the individual herself rather than a proxy respondent. We explore an alternative approach, focused on collecting data on farm and non-farm household enterprises and asking who participates in these activities and in what role, and whether these activities are carried out for the purpose of market exchange or for subsistence purposes. In Egypt, compared to an activities list approach, using data on market activities at the household level and who participated in them, urban women’s employment rate increased from 20 to 24 percent, while rural women’s employment rate increased from 16 to 40 percent (Keo, Krafft, and Fedi 2022).

2
on welfare and the relativist gradient is attributed entirely to social norms. At the upper bound, the relativist gradient is due entirely to social effects, and an absolute welfare comparison calls for a weakly relative poverty line.

The paper provides a new schedule of weakly relative lines that anchors the relative lines to the means from the same surveys used to measure poverty. This is more consistent with past practice in measuring relative poverty than the method used in Ravallion and Chen (2011). It is also means that we are better able to use sub-national data; for example, we can allow for a relative line that is higher in urban areas than rural areas. Second, the paper implements both this approach and the absolute approach on a larger data set than has been used before, adding results from almost 200 household surveys to bring our data base up to 850 surveys, spanning 1979-2011 and 125 countries. The paper presents summary results for the developing world as a whole and by region back to 1981, and up to 2008. Along with this paper, a substantially revised and updated version of the Bank’s website _<u>PovcalNet</u>_ has been produced, which provides public access to the primary data, to replicate our estimates, and to make estimates for selected countries and alternative poverty lines.

The following section describes both our absolute and relative lines, while section 3 describes the data base of household surveys and other data inputs. Section 4 then presents our absolute poverty measures while Section 5 presents the relative measures. Section 6 looks at how our poverty measures compare to a measure of inequality. Section 7 concludes.

# **2. Poverty lines**

_Absolute poverty:_ In setting an international absolute line we follow the same approach used in our past work, namely that the line should be representative of the national lines found in the poorest countries—in the spirit of the original ―$1 a day‖ line (Ravallion et al., 1991; World Bank,
# **6. Empirical Analysis**

This section firstly describes the data employed and illustrates some descriptive statistics. Secondly, the results from the estimation of the model are presented and discussed.

# **6.1. Data and Descriptive Statistics**

The data used in this paper was obtained from Bolivia’s national household survey MECOVI<sup>14</sup> for 1999, 2000, 2001, 2002, 2005, 2006 and 2007. This survey is conducted at the end of each year, typically in November and December.

The age of entry in primary school is six and the duration of compulsory education is eight years. Therefore, samples of children who are between five and fifteen years<sup>15</sup> are selected in this analysis. The academic year is composed of about forty weeks, five days a week and four hours per day.

The focus of the analysis does not encompass private schools<sup>16</sup> , as the enrollment and attendance behaviors are likely to be driven by different factors.

Table 1 shows the proportions of children for each of the above mentioned characteristics.

# [Table 1 about here]

It is relevant to note that the population is fairly distributed in many of its features as gender, ethnic origin, living area and extreme poverty.

In fact, the proportion of male children in the population is roughly the same of the female one. Moreover, the proportion of indigenous children is slightly higher than the non-indigenous one. Likewise, extremely poor children are faintly more than non-extremely poor ones in all but one year (data in 1999 are out of the average because the definition of poor instead of extremely poor has been adopted). In the last two years, the proportion of children coming from urban areas exceeded the one of those coming from rural ones, albeit maintaining a certain level of symmetry in the population.

Finally, children speaking Spanish are more than 90 percent in all years and it is quite unusual for
a somewhat lower share of the regional population.<sup>16</sup> Given that richer countries are more likely to have a survey that can be included in our database, it is not surprising that the GDP coverage exceeds the population coverage. This can also be seen very clearly when we break down the GDP and population coverage by country income status.<sup>17</sup> For example, in 2008 our database covers 97% of the population of the upper middle and high income countries in Africa, compared with 61% for low income countries.

> , where ������� is the CPI in 2011, ���11 is the PPP conversion factor in 2011, and the 2005 terms are defined accordingly.

> 14 For South Sudan we add a 2009 survey directly from household surveys. While the conversion into 2011 PPPs is straightforward, expressing the welfare aggregate in 2005 PPPs is more difficult because national accounts aggregates do not exist separately for Sudan and South Sudan before 2008. Therefore, for the period between 2005 and 2008 we use Sudan’s inflation rate, as well as its 2005 PPP exchange rate.

> 15 This problem is not unique to developing countries. Recently Aguiar and Bils (2015) use the relative expenditure by high- and low-income households on luxuries to show that US consumption inequality is underestimated in the Consumer Expenditure Survey.

> 16 The coverage of Sub-Saharan Africa is very similar in Lakner and Milanovic (2015) who use a somewhat broader set of surveys. On average, they cover 80% of regional GDP and 74% of regional population between 1993 and 2008. 17 Income status as defined by the World Bank Analytical Classification in 2014 (last updated on 1 July 2014). This 2014 classification is based on estimates of gross national income (GNI) per capita for 2013.

5
**_Figure 8.  Market Access Overall Trade Restrictiveness Index, by exporters and markets, 2008_**

<!-- Start of picture text --> Percent<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>High-income East Asia & Europe & Latin America Middle East & South Asia Sub-Saharan<br>countries Pacific Central Asia & Caribbean North Africa Africa<br>Market<br>High-income countries East Asia & Pacific<br>Europe & Central Asia Latin America & Caribbean<br>Middle East & North Africa South Asia<br><!-- End of picture text --> _Source_ : World Bank and UNCTAD staff estimates.

# **The pervasiveness of NTMs is compounded by relatively poor regional trade logistic**

**performance.** Data on the performance of logistics services and on the internal costs associated with shipping goods from the factory gate to the port, and from ports to retail outlets suggest that traders confront significant hurdles in the region, with only the UAE being among the world’s better performers. The _Doing Business_ “cost of trading” data indicate that the costs associated with completing the procedures to export or import a 20-foot container (document and administrative fees for customs clearance and technical control, terminal handling charges and inland transport) remain high in the region (Figure 9).<sup>16</sup> Only Bahrain, Egypt, Qatar, Saudi Arabia, and the UAE were among the 30 top performers in 2009.   Similarly, the Logistics Performance Index (LPI) reveals a great deal of variance and significant performance bottlenecks within many Arab countries (Figure 10).<sup>17</sup> Performance in the mostly high income Gulf states is noticeably stronger, but still well below their income group average. The standout performer is the UAE, ranked 24th in the world in the 2010 LPI,
Table 4: OLS and IV Results Using Different Threshold Levels for the Regulation Dummy

||Number of regressions|where interactive term F<br>significant<sup>1</sup>|DI*Regulation Dummy is|
|---|---|---|---|
|Percentage of most regulated<br>countries for the cut-off point<br>of the regulation dummy|OLS<br>(8 regressions)|IV (2SLS)<br>(8 regressions)|Total<br>(16 regressions)|
|Top 20 percent|6/8 (6 out of 8)|8/8 (8 out of 8)|14/16 (14 out of 16)|
|Top 30 percent|6/8|4/8|10/16|
|Top 40 percent|4/8|0/8|4/16|
|Top 50 percent|0/8|0/8|0/16|

Note:<sup>1</sup> 10 percent significance level or better. The interactive term always has a negative sign.

So far, we have used the aggregated regulation index for the computation of the regulation dummy. In a further set of regressions, we identify those regulation sub-components that drive our results by applying them individually. Out of the five sub-components, starting-abusiness shows the strongest results if we focus on the top 20 percent most regulated countries. In comparison to the aggregated regulation index, the significance levels for the new interactive term, where the dummy is based on regulations on starting a business only, are even slightly higher: For 15 out of 16 OLS and IV regressions, we obtain a negative and significant coefficient for the interactive term (Table 5).<sup>17</sup> For this sub-component, the outcome is very similar if we select a higher threshold level for the dummy, as significance levels of the interactive term drop considerably