rafmacalaba/data-use-mentions-extended huggingface dataset:
unclear what exactly one is measuring with the proxy. To achieve a more objective categorization, this paper uses a clustering exercise to find which variables differentiate landowners the most with the aim of finding an aggregate categorization of ownership.

# **3. Methodology**

In this section, we briefly discuss the two main methodologies used in this paper: Multiple Correspondence Analysis (MCA) and Hierarchical Clustering with Principal Components (HCPC).<sup>17</sup>

# **3.1 Multiple Correspondence Analysis Using Land Rights Data**

Factor Analysis methods, such as Principal Component Analysis (PCA) and MCA are dimensionality reduction techniques. These methods can be used in exploratory data analysis to identify important patterns in the data, particularly when there are a large number of correlated variables. MCA is an extension of PCA that is used when all variables are categorical with multiple levels without necessarily an order. The discussion below will focus solely on MCA given the type of data that we use.

Factor analysis methods have a long history of being used in other disciplines (Abdi and Williams, 2010) and in a number of economic studies to reduce the dimensionality of data. In labor economics, a single measure of skill can be derived using PCA when multiple skill variables are available (see Autor et al., 2003; Antonovics and Golan, 2012). In the macro literature, factor analysis is useful since multiple economic measures are highly correlated. Gregory and Head (1999) used dynamic factor analysis to find a single measure of common economic activity among G7 countries. Factor analysis can also be used in modeling asset returns and economic forecasting (for example Ludvigson and Ng, 2009). In the development context, PCA has been used to create a wealth index. The Demographic and Health Survey (DHS) uses PCA on household durables data to create a single wealth index measure separately for urban and rural households. Most studies that use PCA typically assign the first principal component as the index measure that summarizes
a least-cost route algorithm between any two cells. This way the costs reflect the geography of locations, the availability of different types of connectivity infrastructure, and the prices of different transportation service modes. Migration frictions capture moving costs into and out of a location due to multiple factors, including institutional restrictions, imperfect information, moving costs, and social norms or idiosyncratic preferences for a given location. Desmet et al. (2018) estimate the cost of moving in and out of each location in 2000 using initial technology, amenities, and utility as well as the change in population between 2000 and 2005. Then, they identify migration costs between two locations as the product of an origin- and a destination-specific cost. They proxy initial utility in all locations in each country with the average Cantril Ladder score available for the country from the Gallup World Poll.<sup>2</sup> Thus, if productivity increases due to innovation in a high initial utility location,

> 2 Desmet et al. (2018) assume uniform utility within countries only in the initial period. After the initial period, utility varies both across and within countries.

3
the presence of _disclosure rules_ for politicians. This result may reflect that disclosure rules create stronger incentives for politicians to hide diverted funds on bank accounts in havens rather than keeping them in the domestic financial system where they are disclosed. Column (3) shows that the effect of aid on haven deposits is larger when the country’s _capital account_ is more open. This finding is suggestive that regulatory restrictions on cross-border capital flows may deter diversion of aid through foreign bank accounts. Column (4) shows that the effect varies with _institutional quality_ : it is somewhat larger in autocracies than in countries with more democratic institutions. This is consistent with Andersen et al (2017) who find that the checks-and-balances embedded in democratic institutions are mediating the transformation of petroleum rents to political rents. Finally, Column (5) shows that the effect of aid on haven deposits decreases with domestic _financial sector development_ . This suggests an alternative mechanism whereby firms involved in aid-sponsored projects receive payments in the disbursement quarter and rely on foreign banks for cash deposits when domestic banks are underdeveloped; however, this mechanism is difficult to reconcile with the finding that money only flows to havens around disbursements and not to banking centers without financial secrecy.

Moreover, we exploit the detailed information in the World Bank Project Database to explore differences between two types of aid: Development Policy Financing (DPF) supporting policy programs and Investment Project Financing (IPF) supporting investment projects (World Bank, 2017). One may hypothesize that the latter type of aid, tied to specific expenditure and disbursed over a long time horizon, is more difficult to divert than the former, subject to fewer constraints and disbursed more quickly. However, if anything, aid supporting investment projects correlates more strongly with money flows to havens. As shown in Columns (1)-(3) of Table 5, an aid disbursement of 1% of GDP is associated with an increase in haven deposits of around 2.8% when the aid takes the form of DPF and 5.3%
(4) contain the presence or potential to sustain significant wildlife populations, particularly those of large mammals. Anchor areas under private management were removed from our sample—given our goal of evaluating the impacts of transferring protected area management to private entities—leading to a final control group of 123 government managed protected areas (Figure 1a). We believe this process for constructing our control group strengthens our ability to identify changes in outcomes that are due to AP management, compared to an approach where the control group includes all non-AP-managed protected areas in Africa.

# **2 Results**

To evaluate outcomes between AP and government management, we leverage largescale datasets on wildlife, asset wealth, conflict, and management practices [52]. This comprehensive approach extends prior research documenting the effects of protected areas on land use change [53–55]. Figure 2 displays annual mean values from these diverse datasets in AP-managed protected areas before and after their transference to AP, and in government-managed protected areas. Stark differences in some outcomes pre- and post-transference to AP (Figure 2a,c,d) underscore the rich variation in our data, forming a foundation for a more careful analysis. However, these descriptive statistics are not indicative of AP management effects. Our subsequent differencein-differences analysis, normalizing time relative to transference and controlling for confounding factors, is essential to accurately attribute changes to AP management.

To assess the validity of our difference-in-differences research design, we begin by statistically comparing protected areas managed by AP to our control group of protected areas in terms of variables unaffected by protected area management and in terms of outcomes prior to AP management. Protected areas managed by AP do not statistically differ from the control group in terms of area (km<sup>2</sup> ), longitude, latitude, or annual precipitation (Table S1). However, AP-managed areas experience more extreme heat. They also exhibit uniformly worse pre-period outcomes, though not all differences are statistically significant (Table S2). Prior to transference,
higher than Groups B and C, respectively) or earning an income (17.2 and 32.2 percent higher than Groups B and C, respectively). The bivariate differences across the three groups are statistically significant at 5 percent or lower. In other words, having young children ages 0–5 confers a clear labor market disadvantage for mothers in Groups B and C.

Comparing Groups B and C, which include mothers of young children with different levels of childcare support, gives a sense of the labor market disadvantage that may be driven by access to childcare. Between these two groups, those who do not have access to childcare (Group C) face a larger disadvantage than women who have access to childcare (Group B). Measured with respect to the reference category, Group A, the disadvantage faced by Group C is about twice as large as the disadvantage faced by Group B. More specifically, while the FLFP rate of Group B is only 17.2 percent

> 16 We run Tobit regressions on time spent on secondary childcare with full control variables to check the consistency of the results with OLS estimates. We confirm that findings are reasonably consistent with the findings presented in this paper.

> 17 The survey oversampled the working-age women and men, resulting in a higher female labor force participation rate in this dataset compared to the national statistics for Bangladesh and for urban areas in Bangladesh.

12
between 2010 and 2019, including the 30A poverty reduction program (World Bank, 2022). The question of whether poverty alleviation programs truly assist the poor in escaping poverty in Viet Nam is thus of importance for policy makers and various aid donors, regarding both accurate evaluation of past programs and effective inputs for future policies.

Evaluating poverty alleviation programs that focus on disadvantaged areas, however, presents a challenge due to the nonrandom placement of such programs (Ravallion, 2007; Meng, 2013). In Viet Nam, the 30A Program covers the poorest districts that had a poverty rate exceeding 50% in 2006. This threshold enables us to employ a sharp Regression Discontinuity Design (RDD) method combined with the difference-in-difference method, together with district fixed effects regressions, on rich data from the Vietnam Household Living Standard Surveys spanning the period 2004-2020 to rigorously estimate the program effects on household welfare. Our study thus offers more rigorous evaluation of a large-scale poverty reduction program and covers the longest time period that such a program was in operation to date in Viet Nam.<sup>3</sup> Furthermore, beyond offering new evidence from Viet Nam, to our knowledge, very few existing studies on poverty reduction programs for other developing countries analyze a rich set of outcomes as we do. These outcomes are diverse, ranging from employment (including working rates and work sectors), wages (including incomes from different sources), health insurance (including numbers of health care visits), education (including school enrollment and receipt and amount of school subsidies), poverty measures to household access to credits and loan sizes, and availability of community public services and

> 3 Several other features distinguish our study from these existing studies. While Nguyen _et al._ (2015) employ a difference-in-difference model to analyze household-level outcomes, Phan _et al._ (2016) use a GMM model to analyze province-level outcomes. We employ a more rigorous, combined RDD and difference-in-difference model with district fixed effects and analyze a richer database consisting of household
employment, as share of total<br>70<br>60<br>50 China (2003) Thailand<br>Indonesia<br>Malaysia (1980)<br>40<br>Phillipines<br>30<br>Malaysia (1990)<br>Malaysia<br>20<br>South<br>X axis: GDP<br>Korea<br>per capita,<br>10<br>at PPP,<br>United<br>in 1995 US$<br>States<br>0<br>1,000 10,000 100,000<br>Sources: World Development Indicators and NBS (for China).<br><!-- End of picture text --> 4
bottomup” techniques (Wardrop et al., 2018). Early studies using bottom-up style methods either use simple area measures and/or coarse satellite imagery, or mostly focus on developed countries where there are fewer data limitations (Sutton et al., 2001; Biljecki et al., 2016; Li and Weng, 2005).<sup>8</sup> Second, we compare prediction accuracies between imageries of various resolutions to ascertain if, and by how much, higher resolution imageries are better at predicting density compared to lower resolution imageries. Prior studies have not examined the trade-offs across

> 5 It is important here to clarify that even though we do not directly rely on the census, the census remains essential to provide the sampling frame for subsequent and periodic surveys. Our method, hence, does not reduce the importance of the census.

> 6 For instance, as we will show in section 4, WorldPop 2010 that utilizes the 2001 Sri Lankan census (a gap of nine years) for redistribution, performs much worse than WorldPop 2015 that uses the 2012 census (a gap of three years).

> 7 We also speak to a parallel literature that uses other sources of big data such as mobile phones to map population distribution (Deville et al., 2014) or estimate population maps based on age structure (Alegana et al., 2015), and to an emerging literature that uses satellite data and machine learning techniques to predict human welfare and demographic variables, and urban market boundaries (McBride and Nichols, 2016; Athey, 2017; Engstrom et al., 2017; Jean et al., 2016; Vogel et al., 2018).

> 8 Hillson et al. (2014) use bottom-up techniques but answer a different but related question of what survey size would support a particular confidence level in the estimating population using rooftop area in Bo, Sierra Leone.

4
it allows one to pursue studies at higher schooling levels. This is known as the "option value" of

# secondary schooling (World Bank 1991; Weisbrod 1964).

|Table**10:**Vietnam: Rate of R|eturn using Com|plete Method, 1992-93||
|---|---|---|---|
|Educational Level|Private<br>(no cost)|Private<br>(private costs included)|Social<br>(all costs included)|
|Primary (vs no education)|18.5|13.5|10.8|
|Secondary (vs primary)|5.5|4.5|3.8|
|University (vs secondary)|7.8|6.2|3.0|

Source: VLSS 1992-93, using rate of return program (Psacharopoulos 1996) Social rates of return are highest for primary education. In fact, the earnings data used in these calculations suggest that only primary schooling was a worthwhile social investment in Vietnam in the early 1990s. University education had a low social rate of return, but recent evidence from a survey of higher education graduates indicates that earnings for graduates may have increased in recent years. The difference between the social and private returns suggests an over-subsidization of higher education. However, tuition fees were introduced at the university level in 1993, and the government plans to increase cost recovery in higher education, so this gap may be reduced in the future.

# SUMMARY AND CONCLUSIONS

The returns to schooling in Vietnam are low by international standards. The estimates come from earnings data for 1992-93. These are the best data currently available, but may be distorted for a number of reasons. First, public sector pay reflects the past policy of assigning secondary and higher education graduates to the public sector. Also, we have limited the analysis 22
form mis-specication is not responsible for the observed relations.<sup>7</sup> Taking advantage of data on cognitive ability in IFLS 2014 in Indonesia, we explore whether the estimated eect of father's education is largely due to omitted ability heterogeneity of children. We nd that the inclusion of quadratic controls for ability reduces the magnitude of the impact of father's education on conditional variance, but the estimates still remain substantial and statistically signicant at the 1 percent level.

For relative mobility, the estimates of risk adjusted IGRC (RIGRC) suggest that the workhorse measure of relative mobility in the literature, IGRC, substantially underestimates the impact of family background. The estimates for the full sample (1950-1989 cohorts) suggest that the extent of underestimation on average is 26 percent in China, 41 percent in India, and 10.4 percent in Indonesia.<sup>8</sup> Accounting for the inuence of family background on conditional variance of schooling makes a dramatic dierence in the estimated relative and absolute mobility for the children born to the most disadvantaged households (fathers with no schooling).<sup>9</sup> RIGRC estimates from the full sample (1950-1989 birth cohorts) for this subgroup shows that the standard IGRC overestimates relative mobility by 37 percent in China, and by 63 percent and 28 percent in India and Indonesia respectively. In contrast, the gap between the RIGRC and IGRC estimates for the subgroup with college educated fathers is small. Absolute mobility is also substantially overestimated for the most disadvantaged subgroup without risk adjustments: conditional mean of years of schooling is overestimated by 48 percent in China, 127 percent in India, and 25 percent in Indonesia. Again, for absolute mobility of the children of college educated father, the risk adjustments does not make any substantial dierence. The upshot is that while the standard estimates of relative and absolute mobility seem to capture reasonably well

> 7Based on recent evidence, we allow for a quadratic mobility CEF
.0384)||0.0373<br>(0.0423)|
|City: Other urban # SPEI: Rainy||0.0558<br>(0.0768)||0.0541<br>(0.0370)|
|Household FE|No|No|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Adjusted R2|0.0312|0.0313|0.0097|0.0099|
|# of observations|47,795|47,795|47,795|47,795|
|# of households|18,490|18,490|18,490|18,490|

_Note_ : The table summarizes the estimation results of panel regression models in Equation (3) for households in the five waves of IFLS (1993, 1997/8, 2000, 2007/8, and 2014/15). The dependent variable is a binary indicator about household’s vulnerability status (1=not vulnerable; 0=vulnerable). Cluster robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

# **5. Discussion and conclusion**

This paper examines the effects of the climatic and environmental shocks on a key function of urban agglomerations to facilitate poverty reduction. Our study also showcases different empirical approaches depending on the availability of panel datasets. We constructed synthetic panel datasets for Colombia and Chile from repeated cross-sectional household surveys and examined the association between poverty changes over time and the city population size as well as the heterogeneity of such association by flood risks. By estimating two-way FE models with five waves of IFLS spanning from 1993 to 2015, we analyzed the probabilities of households escaping poverty in different locations—metro core, urban periphery, other urban areas
Table 5 shows that of 51,976 GGUs, 1,131 units in the top 5 deciles account for 50% of global methane emissions. For these high-priority GGUs, we simplify the monitoring problem by confining our exercise to emissions-intensive (5-decile) EDGAR cells in the set of 21,057 cells that account for 50% of global methane emissions (Table 4). Within each GGU, we compute the convex hull that includes all EDGAR 5-decile cells in the unit, construct a 20-km buffer around the hull, and intersect the buffered polygon with the GGU boundaries to define the sampling area for Sentinel-5P observations. This generates sampling areas for 1,001 high-priority GGUs.<sup>10</sup> Figure 5 maps these units, revealing significant representation for on- and offshore GGUs in all world regions.

# **4.  Sample Data**

We have constructed our atmospheric CH4 concentration database from observations by the ESA’s Sentinel-5P platform that typically number over 200,000 per day. We locate each observation in a 5-km global grid comprising 25,920,000 cells. The dominant component in each observation is the global stock of atmospheric CH4 molecules that have accumulated during the past decade. The second component is seasonal, determined by natural processes over the annual cycle. The third component is geographic: Emitted CH4 molecules tend to remain within their latitude bands of origin for significant periods before full global mixing.

The fourth observation component is determined by local anthropogenic CH4 emissions, which remain near their point of origin long enough to be distinguished from the three “background” components. We term this the “concentration anomaly” since it measures the local deviation from the global background CH4 concentration. As previously noted, its primary sources are emissions from agriculture, fossil fuel production and waste disposal.

Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and
CAP and the Regions: The territorial impact of the common agricultural policy” by Shucksmith, Thomson and Roberts. Following their work in the early 2000s, we attempt to re-examine social impacts of the CAP after its major reforms in 2003. More accurate CAP data become available for us in the CATS database, along with agricultural data at NUTS3 level from Eurostat’s Farm Structure Survey. In addition, more countries started receiving CAP funds. To capture this difference, we provide one set of results using all EU members and another set of results using only the EU15 in our replications of results of Shucksmith et al.

**Table 1.** Pearson correlation coefficients between level of total Pillar 1 support accruing to NUTS3 regions (2008-2013) and socio-economic indicators (2011)

|Variables|Unemploym<br>ent rate|Povertyrate|Share of the<br>country<br>poor|GDP per<br>inhabitant|Population<br>change<br>(2008-2013)|Support per<br>hectare<br>UAA|Support per<br>AWU|
|---|---|---|---|---|---|---|---|
|||||**EU15**||||
|Support per hectare UAA|0.1163|0.0630*|0.1420**|0.1636**|0.0853**|1|0.6479**|
|Number of observations|193|963|963|963|963|963|962|
|Support per AWU|0.0942|0.0846**|0.1540**|0.0651*|0.1954**|0.6479**|1|
|Number of observations|193|964|964|964|964|962|964|
|||**EU**|*
No. of observations(countries)|215|56|23|84||43||

_Notes:_ The table shows unweighted averages of country characteristics. It includes 215 countries of which 107 are either investors or host countries in the period October 1, 2008 to August 31, 2009 and 33 countries are both host countries and investors. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

(a) Significance of t-test for difference between averages in destination countries and countries of origin only.

(b) Significance of t-test for difference between averages in destination countries with implemented projects and destination countries where no project is under production.

Other notes: Food dependence is defined as the value of net food imports per inhabitant. It is positive for net importers and negative for net exporters; Suitable non-forest land with low population density is the amount of potentially suitable land that is neither currently used for agricultural production nor settled with more than 25 inhabitants per km<sup>2</sup> is calculated as discussed in the text and excludes protected areas (Source: FAO and IIASA); The value of potential output is obtained for choosing the best culture as the market prices and yields. It is expressed in logarithm of the value in millions of dollars (Source: FAO and IIASA); The yield gap is the difference between performance that is technically achievable and the effective yield observed (Source: FAO and IIASA); The variables Regulatory Quality, Rule of Law, Control of Corruption, Political Stability and Absence of Violence, and Voice and Accountability are from the Worldwide Governance Indicators database and are expressed in percentile of the distribution of descending rank (Source: World Bank). A low value thus characterizes a country where governance is poor; The index of weak protection of investors is the rank of the variable Strong investor protection in the Doing Business database and is comprised between 1 and 215 (Source: International Finance