rafmacalaba/data-use-mentions-extended huggingface dataset:
means that by alternating the population census with the agricultural census we are able to triple the frequency of poverty and inequality estimates at the small area level. The latter is important as it makes the small area estimation exercise a more suitable tool to monitor poverty and inequality over time, channel resources when and where they are most needed, and to evaluate poverty reduction initiatives across the different areas in Vietnam.

While replacing the population census with the agricultural census does not require any methodological changes, there are some differences worth noting. Most importantly, the agricultural census only allows us to provide estimates for rural Vietnam, where the population census covers both rural and urban areas. Also, the two different census data sets each have their own specific variables, in addition to a standard set of 2
Figure 6: Distribution of poor population across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of poor population (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Each boxplot shows the distribution of the share of the poor population over different geographic areas in 16 SSA countries.

Figure 7: Comparison of urban shares of poor populations between official and DOU/DB urban definitions in 16 SSA countries <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>100 100 100<br>80 80 80<br>60 60 60<br>TCDETH TZA GAB TCD ETHGABTZA<br>UGA NERUGA NER UGA<br>40 TZA AGOGAB 40 MWI LSO AGO 40 LSOMWI AGO<br>ETH CIV BFA SENMRC TI V BFAMRTSENCIV<br>20 BFANERGINMWITCDGNBSENLSOMRTGHA 20 GINGNBGHA 20 GINGNBGHA<br>20 40 60 80 100 20 40 60 80 100 20 40 60 80 100<br>Share of poor in urban area (%) Share of poor in
0.581|

## _Source:_ Authors’ estimates.

_Note:_ Columns 1-4 report estimations with annual-frequency data. Columns 5-6 report results with non-overlapping 5-year averages (the following periods: 2002-06; 2007-11; 2012-16; 2014-21). Column 5 shows results with control variables lagged by one period, column 6 shows results with control variables not lagged. *, **, *** indicate statistical significance at the 10, 5, and 1 percent levels, respectively. Country-level clustered standard errors are in parentheses.

36
Policy Research Working Paper 8917

# **Abstract**

Research on the determinants of collective action in the commons generally focuses on interest-group heterogeneity, implicitly assuming that groups perceive the same problems but have different priorities. This paper changes the focus to the role played by perceptions themselves. Within localities, collective action may be easier if elite and non-elite households have similar perceptions of environmental problems. Regionally, collective action may be aided by common perceptions among local elites who communicate across village lines. This paper uses regression analysis to explore variations in environmental perceptions across classes and localities, using new survey data from the Indian Sundarbans. The paper finds that perceptions vary significantly across localities. Within localities, perceptions among elite households vary significantly more than perceptions among non-elite households. The results therefore favor locally-oriented collective action in the region, along with local governance that promotes non-elite participation.

This paper is a product of the Development Research Group, Development Economics and the Environment and Natural Resources Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
probability distribution function for a standard normal random variable, and let zit denote the poverty line for household _i_ and time _t_ . The conditional probability of being poor is seen to solve: where σ<sup>2</sup> denotes the variance of _u_ . The unknown model parameters will in practice be replaced by estimates, so that the estimated poverty rate solves:

**Remark 2** : It is important to note that the imputation-based poverty estimate will be subject to both sampling error and model error. Standard errors can be estimated accurately by means of bootstrapping, which will be our method of choice in the empirical section of this paper. The HES will be bootstrapped to capture the model error, and the LFS will be bootstrapped to capture the sampling error, see Fujii and van der Weide (2012, 2013) for more details. For asymptotic standard errors that account for the dual error structure, also see Fujii and van der Weide (2012, 2013). Note that ignoring this error structure, for example by treating the imputed data as observed data, will lead to underestimation of the standard errors and thereby to overestimation of the poverty estimates.

**Remark 3** : The poverty line must be measured in the same time _t_ prices as the expenditure (or income) variable that was used as the dependent variable in the regression model. In other words, if a model is estimated on 2001 household expenditure data (measured in 2001 prices) and subsequently applied to a 2007 LFS, then the imputed data will represent 2007 expenditure measured in 2001 prices. As a result, this imputed expenditure would have to be compared to the 2001 poverty line in order to obtain an estimate of 2007 poverty (see also Remark 1).

**Remark 4** : The approach has the best chance of identifying trends in poverty if the changes over time can be traced back to changes in the observed independent variables (e.g. changes in education levels, employment characteristics, and
from the authors). But after including job market and work characteristics, the coefficient becomes insignificant (Table 6, columns 1 to 6, except 3) or negative (Table 6, column 3). It is hazardous conditions, concern about job stability and being paid, partly or fully, off the record (i.e., envelop wages) that are associated to dissatisfaction. The inclusion of these variables removes the significance of the informal employment variable. The lack of statistical significance of type of job contract (formal vs informal) is seen both for all informals (columns 1, 3 and 5) and salaried informal workers (columns 2, 4 and 6). The exception, column 3, indicates that informals are less dissatisfied with work pay, which seems to confirm a view of informal workers not being systematically disgruntled with their job (more on this finding later).

Informal workers are also significantly more likely to receive envelope wages. Receiving part of the wages off the record is an employment practice that is commonly associated with informal employment and still prevalent in some CIS and Central and Eastern European countries. The decision to pay or receive payment in envelope wages is often driven by an intent to evade taxes, or to ease tight economic conditions, and is known to often cover remuneration for regular work hours. The RLMS asks whether all, part or none of a worker’s wage payment was transferred unofficially. Table 6, shows that informal workers, both using total and salaried-only samples (columns 7 and 8, respectively) are more likely than formal workers to receive envelope wages even after controlling for personal and job market characteristics.

Although more prevalent, envelope payments are not exclusive among informal workers. The incidence of envelop wages varies markedly by type or work. About 10 percent of formal workers are paid part or all their earnings off the record. In contrast, among salaried informal workers (i.e., in a firm but without a contract), 91.5 percent
individual financial institutions in East Asia (one study is Laeven, 1999). Using individual institution data, one can investigate, for example, why, despite the fact that all financial intermediaries faced similar macroeconomic shocks, not all experienced distress and/or eventually failed. One can thus identify the specific characteristics of distressed (or failed) institutions compared to non-distressed (or non-failed) institutions; these characteristics can be used in developing systems to monitor the risk of distress of financial institutions in the future. By studying the resolution and closure processes, one can try to identify what type of processes used to resolve distressed financial institutions are most adequate and lead to the least loss of confidence.

Our work also relates to the literature on predicting individual financial institutions' distress and closures. Models trying to predict the failure of individual financial institution ("early warning systems") have been developed since the '70s. Mainly applied to developed countries' banking systems,<sup>3</sup> these studies focus on the early identification of financial institutions, which are developing financial difficulties. From a banking regulator and supervisory agency's viewpoint, early warning systems can help minimize the use of relatively scarce examination resources while at the same time achieving as much failure-prevention as possible. Indeed, failure prediction models and early warning systems have proven important tools for supervisory agencies to schedule individual on-site bank examinations and initiate remedial actions.

The first generation of financial early warning systems aimed to build screening devices to help in scheduling bank examinations by flagging as early as possible those institutions in (or approaching) financial distress. These studies share a similar approach (Meyer and Pifer 1970; Sinkey 1975; Altman 1977; Martin 1977; Pettaway and Sinkey 1980; see Altman 1981 for a comprehensive survey of the early wave of the literature): on the basis of a set of financial ratios, reflecting the different dimensions of a CAMEL rating system,<sup>4</sup> the statistically best subset of variables is chosen to distinguish
||
|1990 - 94|64|95|65|56|
|2010 - 14|77|94|93|70|
|**Herfindahl Index**|||||
|Product Concentration|||||
|1990 - 94|0.41|0.84|0.69|0.29|
|2010 - 14|0.30|0.67|0.46|0.21|
|Market Concentration|||||
|1990 - 94|0.20|0.30|0.08|0.19|
|2010 - 14|0.20|0.15|0.28|0.18|
|**Natural Resources**|||||
|Percent of GDP|||||
|1990 - 94|17.0|46.9|26.8|9.8|
|2010 - 14|13.2|43.0|16.3|8.9|
|Percent of total exports|||||
|1990 - 94|0.82|0.99|0.95|0.78|
|2010 - 14|0.73|0.89|0.71|0.72|

_Sources: WDI, and author's calculations using WITS, COMTRADE_ _page 11_
(0.013)|
||[0.769]|[1.000]|[1.000]|[1.000]|[0.747]||
|Observations|827|826|827|827|827|827|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|0.000|0.000|0.000|
|Diff. p-value|0.171|0.673|0.698|0.631|0.456|0.741|
|YWYjoint test|0.443|0.364|0.101|0.878|0.005||

_Notes:_ This table reports OLS estimates from Equation 1. Dependent Variables: (i) Migrants Benefit the Economy: z-scored response to the statement, “Immigrants are good for a country’s economy” on a four-point scale from 1 (strongly disagree) to 4 (strongly agree). (ii) Noncompeting Migrants in Host’s Jobs: z-scored response to the statement, “Immigrants come to compete for our jobs” on a four-point scale from 4 (strongly disagree) to 1 (strongly agree). (iii) Criminality Unaffected by Migrants: z-scored response to the statement, “Immigrants increase crime” on a four-point scale from 4 (strongly disagree) to 1 (strongly agree). (iv) Migrants Enrich Host’s Culture: z-scored response to the statement, “Immigrants improve our society by bringing new ideas and cultures” on a four-point scale from 1 (strongly disagree) to 4 (strongly agree). (v) Migrants’ Positive Contribution: z-scored response to the statement, “In general, immigrants contribute more to a country than they take away from it.” (vi) Positive Beliefs Index: Constructed using dependent variables of columns (1) to (5) following the methodology of Kling, Liebman
, the census data in the focus countries of this paper are not up to date, we explore the use of more current geospatial data as covariates instead of old census data.

We opt for a household-level model of welfare over a grid cell-level model of poverty rates because it utilizes more detailed information about the distribution of the welfare variable, and it is easier to interpret. In addition, defining the grid cell-level poverty rate as the outcome to be estimated, as in the case of an area-level model, requires accounting for the corresponding sampling variability, which may be challenging at such a small level of aggregation. We also prefer a household-level model to an area-level model because the former allows for the use of auxiliary data at the grid cell level rather than at the target area level, which can improve the accuracy and precision of the estimates, as demonstrated in Masaki et al. (2022) and Newhouse et al. (2022).

We model the household log per capita consumption as a linear function of a subset of geospatial covariates selected through Lasso. The procedure is described in detail in Appendix B. The model equation takes the form: l r r 1 r 2 3 r , (1), where l r represents the log per capita consumption of household YY rrh = XX rrββ + XX ββ + DDrrββ + ννrr + εε rrh _h_ , for which the centroid of their survey enumeration area falls in grid _g_ within target area _a_ and region _r_ . This rrh YY value of consumption has been spatially deflated using estimated local prices. r represents the vector of grid cell geospatial zonal statistics, and r represents the vector of unweighted rr XX averages of the geospatial variables at the target area level. represents a set of regional dummy variables, is a random effect specified at the target area level with XX ) , and rr r is a household-specific error term with r ) . Survey weights are incorporated DD rr rr vv<sup>2</sup> νν νν ~ NN(0, σσ
_HOTTER PLANET, HOTTER FACTORIES_ _19_ in the non-agriculture sector is still developing, relatively recent studies document estimates that are comparable to those from this study. Using data from half a million manufacturing firms in China, Zou and Zhong (2022) find that a day with a temperature above 90<sup>0</sup> F (32<sup>0</sup> C) is associated with a TFP loss of 0.56 percent, relative to a day with a temperature between 50<sup>0</sup> F (10<sup>0</sup> C) and 60<sup>0</sup> F (15 _._ 6<sup>0</sup> C). In a study of Indian manufacturing firms, Somanathan et al. (2021) find that plant output falls by about 2 percent per 1<sup>0</sup> C increase in temperature. In a US study, Deryugina and Hsiang (2014) show that the productivity of individual days declines by 1.7 percent for each 1<sup>0</sup> C (1 _._ 8<sup>0</sup> F) increase in daily average temperature above 15<sup>0</sup> C (59<sup>0</sup> F). Using data from personnel records, Cai et al. (2018) find that productivity at temperatures below 60<sup>0</sup> _F_ (15 _._ 6<sup>0</sup> C) is about 11% less than that at 75<sup>0</sup> F and 79<sup>0</sup> F. The productivity losses associated with higher temperatures that we find in this study are relatively larger compared to these and other similar studies. The main reason is that our study identifies nonlinear impacts at the higher end of the temperature distribution, while
In order to not give undue weight to these surveys, the results on poverty and schooling for datasets that identify fewer than 50 children with a disability are not reported.<sup>6</sup>

**<u>Table 3. Number of youth 6 to 17</u>**<sup>**+**</sup> **<u>defined as having a disability in each survey</u>**

|_Country andyear_|_Type of survey_|_Maximum_<br>_age _|_Number_<br>_of youth_|_Number of youth with_<br>_a disability_|
|---|---|---|---|---|
|Burundi 2000|MICS2|14|5,865|73|
|Cambodia 1999|SES|17|10,881|96|
|Cambodia 2000|DHS|17|23765|214|
|Indonesia 2000|SES|17|64,136|326|
|Jamaica 1998|LSMS|17|6,964|58|
|Jamaica 2000|LSMS|17|1,640|14|
|Mongolia 2000|MICS2|17|7,645|245|
|Mozambique 1996|NHS|17|14,520|156|
|Myanmar 2000|MICS2|14|26,329|41|
|Romania 1995|LSMS|17|13,777|82|
|Sierra Leone 2000|MICS2|17|7,534|28|

_Note_ : Data are unweighted in order to show the actual number of observations underlying the analysis. +Maximum ages are 18 in Mongolia, and 14 in Burundi and Myanmar.

A last data constraint concerns the measurement of household poverty. All LSMS and SES surveys include household per capita consumption expenditures (PCE), the variable typically used in poverty analysis. DHS and MICS2 data, however, do not include those variables. In this study, quintiles based on
# **5 Conclusion**

This short analysis of UNHCR data provides several insights. First, it shows that available refugee data can be used to clarify some important parts of the policy debate. Much of this debate has been informed by advocacy on the one side, and xenophobia on the other. Using evidence can help provide a more nuanced perspective of a complex issue.

Second, it highlights the limitations of available data. In particular in the absence of individualized data, it is not possible to calculate the average duration of exile, but only an over-estimate. It is also not possible to calculate an expectancy which could be useful for policy makers. Should individualized data be available, such calculations would become possible.

Third, it provides an over-estimate of the mean duration of exile at around 11 years and of the median duration at about 4 years. The mean duration of exile has been fairly stable since the late 1990s, at 11 to 15 years.

Fourth, it underlines the plight of those who are in protracted exile. Their number has remained steady since the mid-1990s, at 5 million to 7 million people. The mean duration of their plight, however, has gradually increased and exceeds 20 years.

Fifth, it suggests that all these numbers are very sensitive to two country contexts: Afghanistan, which pushes all averages up (and without which the average duration of protracted situations is reduced by almost half) and the Syrian Arab Republic which still brings them down. Yet, when both situations are excluded, neither the mean nor the median varies substantially.

And sixth, it shows that in many cases a reduction in the average duration of exile is the result of an inflow of new refugees, and hence of an aggravation of the situation.

Unfortunately, comparable data are not available to carry out similar calculations for IDPs. The definition of a “protracted situation” for IDPs is also complex, considering that there is no clear definition of what would constitute a measurable end-point for their ordeal. Case studies may
term becomes insignificant. A look into the underlying data reveals what is going on. Countries with completed spells

> 19We have also taken a less radical approach and interacted our three variables of interest with an Africa dummy. In every case, the added interactions are individually and jointly insignificant at the 5% level. We take this as further evidence that our results can be generalized to settings outside of Africa, such as Asia, where institutions are stronger and linguistic diversity is comparable. We thank an anonymous referee for pointing us towards this test.

27
<br>Household total expenditure (Rs.)|37769|37807|37755|0.04|
|Household consumption (Rs.)|9274|9367|9239|0.45|
|No. of observations|4,275|1,191|3,084||

Note: Distance to railway station, bus stand, post office, telephone office, weekly market, and all the other schemes between the reserved and unreserved groups are not statistically significant. The column „test for equality‟ reports the |t|-statistic for the test of equality of means.

21
1 percent in 1992. These findings indicate that education is a very important key to poverty reduction in rural Mexico. It is not a silver bullet, however, as the more educated are increasingly experiencing poverty, something that was rare a decade ago.It is useful to compare the characteristics of the extreme poor across rural areas to gain information about the spatial dimension of poverty. Geographic factors are important when analyzing poverty in rural Mexico. Table 4.1 and Figure 4.1 show the variation in P0 across the rural areas with less than 15,000 inhabitants by region and education level attained. The Center, Gulf, and South regions have the highest headcount poverty rates for all levels of education in rural Mexico and, in some cases, it is more than 10 times higher than in the North or Capital regions such as the case of completed primary 19
Figure 6. Country Cases: Weakening Severity (Rows Ranked by High, Medium, Lower and Low Peak Severity) <!-- Start of picture text --> Belgium Spain United Kingdom Italy<br>150<br>100<br>50<br>1 1 1 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Germany Iran, Islamic Rep. Turkey United States<br>50<br>40<br>30<br>1<br>20 1 1<br>1<br>10<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>China Czech Republic Algeria Greece<br>10.0<br>2<br>7.5<br>3<br>5<br>5.0<br>7<br>2.5<br>18<br>0.0<br>Feb 1Mar 1 Apr 1May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Australia Korea, Rep. Morocco Malaysia<br>2.5<br>9<br>2.0 12 11<br>1.5 20<br>1.0<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health