rafmacalaba/data-use-mentions-extended huggingface dataset:
sources. Administrative data point towards clear discrete changes starting with adoption of regulatory reforms and sustained since in terms of (i) a reduction of the time needed to register transfers or mortgages; (ii) an increase in the share of newly registered plots owned (jointly or exclusively) by women matched by a decline of those owned exclusively by men; (iii) an upward shift and acceleration of the number of registered transfers and mortgages; and (iv) expansion of registry coverage resulting through a burst of project-supported systematic plot registrations from 2011 to 2013 and a more gradual growth of the number of plots registered sporadically.

Yet, a traditional DID approach on a matched household sample rejects the hypothesis of no significant effect on land markets or credit access. To probe if this reflects reality or may be attributed to measurement error or methodological issues such as the inability of a DID approach to identify effects of policy change that affect treatment and control areas equally, we use administrative data to implement an approach using block fixed effects that also allows identifying the effects of policy change separately from those of systematic registration. We implement this by dividing the entire project area into 1,932 blocks of 250*250m each and aggregating georeferenced registry and cadaster data at block level for each of the 20 1
On the demand side, borrowers who were approved for a loan from our partner bank may have updated about their own creditworthiness and engaged in more vigorous search for alternative sources of credit. In that case, competing financial institutions would be responding only indirectly to the loan offers of our partner bank. We view this mechanism as inconsistent with the fact that we find no effects on borrowers with an established credit history, who would presumably be similarly emboldened. But we note the borrowers with established credit histories who are approved as a result of surpassing the screening threshold are those who could not receive a loan on the basis of their credit history alone. Thus, it is possible that they did redouble their efforts to find an alternative source of credit but that these efforts were fruitless. This interpretation is consistent with the results of Karlan and Zinman (2009), who find that borrowers randomly selected to receive a loan updated their perceptions about their own creditworthiness.

On the supply side, we can rule out mechanisms in which information spillovers occur through the credit registry, as loan approvals are not recorded in the Peruvian credit registry. And on the demand side, we can rule out that increased demand for loans from competitors was driven by increased loans from our partner bank and complementarities in demand for credit. Of all thin file borrowers to the right of the EFL threshold in the bandwidth of our primary specification, only a quarter of those who took loans from competing lenders also took a loan from our partner bank.

While we cannot pin down a single mechanism, we note that the consequences of each of these are largely the same. In either case, the additional loans resulting from our partner's efforts to expand financial inclusion were primarily issued by other financial institutions. Whether this was a direct response to the loan approvals of our partner bank, or merely an indirect consequence, the justification for subsidizing the expansion of financial inclusion remains.

15
Insight Crime (2022b). Venezuela’s cocaine revolution. _Insight Crime_ .

- Insight Crime (2023a). Dutch Caribbean remains a high-risk route for Venezuelan migrants. _Insight Crime_ .

- Insight Crime (2023b). Venezuelan migrants remain easy prey for organized crime. _Insight Crime_ .

- Insight Crime (2023c). Venezuelan security policy: The criminal exploitation of the migrant crisis. _Insight Crime_ .

- Insight Crime (2023d). Venezuela’s corrupt officials key to human trafficking networks. _Insight Crime_ .

- Instituto Geogr ́afico de Venezuela (2015). Venezuelan roads shapefiles.

- International Monetary Fund (2024). World Economic Outlook 2024.

-

- IPUMS (2023). XII censo general de poblaci ́on y vivienda 1990 IPUMS subset.

- Javorcik, B. S., Ozden,<sup> ̈</sup> C ̧., Spatareanu, M., and Neagu, C. (2011). Migrant networks and foreign direct investment. _Journal of Development Economics_ , 94(2):231–241.

- Kapur, D. (2014). Political effects of international migration. _Annual Review of Political Science_ , 17(1):479–502.

- Karadja, M. and Prawitz, E. (2019). Exit, voice, and political change: Evidence from Swedish mass migration to the United States. _Journal of Political Economy_ , 127(4):1864–1925.

- Kerm, P. V. (2020). SGINI: Stata module to compute generalized gini and concentration coefficients, gini correlations and fractional ranks.

- Khanna, G., Murathanoglu, E., Theoharides, C. B., and Yang, D. (2022). Abundance from abroad: Migrant income and long-run economic development. Technical report, National Bureau of Economic Research, Cambridge, MA.

48
<!-- Start of picture text --> 3 8Nn gt sa<br>2 — °<br>5 mr. gg © °<br>gs , an<br>zyeS r’ Ss<br>So a a” ° Se<br>2<br>aS52 Cs ry © 8<br>g r ° ¢<br>id a £ } & a A A<br>“GI f 8a wd ‘ AAAAS<br>oéa4<br>050000 =100000 150000 200000 250000 300000 350000 400000<br>Cost of Targeting Travelers in $<br>4 Target Areas: Use Cell Phone Data © Target Travelers:Incidence in 2012<br>4 Target Travelers: Use Cell Phone Data 4 Target Areas and Travelers<br><!-- End of picture text -->
climate in the former Comecon countries.

Our results can also inform the discussion of policy instruments for CO2 emissions reduction. Many policy analysts who support Pigouvian pricing also argue for a non-Pigouvian supplement: coordinated public investment in low-carbon land development, energy and transport that will accelerate the transition to low-carbon economies. We explore the non-Pigouvian proposition for subway investments, drawing on a recent global survey of subway systems. We divide our 1,236 regression residuals into deciles, with the largest negative residuals in the first decile, and identify the subset of 132 global subway cities in each decile. We find that subway cities are four times more numerous among first-decile cities than among tenth-decile cities. We also find that representation of subway cities declines steadily across deciles. While these results provide strong suggestive support for the non-Pigouvian view, they are subject to potential endogeneity that should be considered in future research.

This research is only one of many potential applications for our global CO2 database, which covers all terrestrial areas at 10 km resolution. The same CO2 emissions model could be used to track progress for large and small cities within subregions or countries, subregions within countries, or project areas.

2
# 1. Introduction

Most social programs in developing countries are based on the explicit objective of reducing poverty (Elbers et al. 2007). Targeting is centered on successfully identifying poor households. However, this prioritization ignores the intertemporal dimension of poverty. Nonpoor households are also at risk of becoming poor. The exposure to natural shocks, pandemics, economic crises, and climate change means there is a need to rethink social assistance to cover both the poor (improving incomes and therefore reducing poverty) and providing insurance against risks (reducing the likelihood of falling into poverty). As an integral part of designing a new generation of social assistance programs that aim to sustain previous poverty gains, it is necessary to develop new targeting mechanisms that cover currently poor households and those that are vulnerable to poverty.

We develop a novel method for producing vulnerability maps for small administrative areas combining information from a household survey and a national census. The approach integrates insights and techniques from the literature on small area estimation and the vulnerability to poverty. While conventional poverty mapping focuses on estimating the share of households that are currently poor (poverty-induced vulnerability), the proposed method also enables the estimation of the share of nonpoor households that are highly likely to fall into poverty (risk-induced vulnerability). Following the vulnerability literature, the analysis relies on a definition whereby households are considered at risk of poverty if the predicted probability that a household will become poor in the next two periods is above 0.5. A complete description of poverty and risk-induced vulnerability is provided in Günther and Harttgen (2009); implementation details can be found in the methodological section.

The application of the method described here involved the creation of a comprehensive poverty and vulnerability map of Senegal. The map offers a detailed spatial analysis of poverty-induced and riskinduced vulnerability across the country. The overarching findings indicate elevated poverty rates in rural areas and the southeastern regions. However, the results also reveal a more nuanced picture of poverty and vulnerability. Specifically, though they exhibit lower poverty rates, urban communes
# **Figure 9: Skilled and unskilled labor content of exports across sectors, South Africa 2011**

<!-- Start of picture text --> Minerals nec<br>Metals nec<br>Machinery and Equipment nec<br>Ferrous Metals<br>Transport Equipment<br>Chemical, Rubber, Plastic Products<br>Trade and Transport Services<br>Other Private Services<br>Agr, Forestry, Fisheries<br>Processed Foods<br>PubAdmin/Defence/Health/Educat<br>Energy Extraction<br>Paper Products, Publishing<br>Metal Products<br>Beverages and Tobacco Products<br>Manufactures nec<br>Wood Products<br>Textiles<br>Mineral Products nec<br>Wearing Apparel<br>Electricity, Gas, Water<br>Leather Products<br>Construction<br>Dwellings<br>0 2,000 4,000 6,000<br>US$ mil.<br>Total LVAX Unskill ed Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

Manufacturing matters for jobs, but its importance comes mainly through its backward linkages to the domestic economy. Replacing the underlying labor value added share in production (from national accounts contained in GTAP) with the number of jobs contained in production (from ILO), we can isolate the number of direct and indirect jobs contained in exports. Figure 10 shows the direct and indirect jobs in exports for 2011. Due to differences in sectoral aggregations across data sources, we can only group the sectors into 11 macro sectors. Manufacturing dominates the ranking, responsible for almost two-thirds of all jobs contained in exports. In 2011, manufacturing exports contributed 1.9 million jobs. The vast majority of these jobs are created in sectors providing inputs to manufacturing (1.5 million indirectly and 0.4 million directly): the indirect employment impact of manufacturing
an independent Markov Chain by using Tauchen & Hussey (1991). Furthermore, the differences between Core and Periphery are not only in their income process, but also the Core is richer than the Periphery by 20% on average according to Eurostat data. A period in the model refers to a quarter, and the risk free interest rate is set equal to 1.7% as in Arellano (2008). The utility function displays a constant coefficient of relative risk aversion form, The risk aversion coefficient _σ_ is set to 2, which is a common value used in real business cycle studies. All the Eurobonds proposal had some form of borrowing limit for the member states in order to mitigate moral hazard concerns. For this reason, I set an exogenous borrowing limit, of 66% debt-to-income ratio for the Core and of 83% for the Periphery.<sup>8</sup> As in Arellano (2008), I assume that default entails some direct output cost of the following form: where _γ_ is the exogenous output cost that I set equal to 0.96, as in Arellano (2008). Finally, I calibrate the discount factors of the benchmark model to match the sovereign spreads of Core and Periphery and I set them equal to 0.89 and 0.88, respectively.

Table 2 presents some results on the performance of the benchmark models in comparison with the data. To derive the business cycle statistics, I run many simulations of the model over time until a default occurs and I evaluate the mean statistics of these simulations.

The model matches relatively well the spread for both countries. It predicts that the mean interest rate spread for the Core is 0.5%, while in the data is 0.6%. The model is less successful for the Periphery, since it generates a mean interest rate spread of 1.9%, while in the data is 2.4%. Moreover, the model has an exogenous debt-to-output ratio to match the data.
characteristics that proxy for different types of innovation: the share of firms that introduce new products, use new methods of production and contribute to R&D spending. Third, we use three characteristics indicative of “learning-by-doing”: the share of large firms (indicating potential for scale economies), the share of firms using a licensed technology from a foreign-owned firm, and the share of firms with formal training programs (indicating on-the-job learning). Last, we analyze three characteristics indicative of the nature of factor use: capital expenditure per employee (measuring capital intensity), years of schooling (measuring skill intensity), and the (full-time) employment elasticity of output. They reveal the importance of physical and human capital, relative to labor, in the organization of production and thereby reflect the ease of securing employment for a predominantly unskilled labor force.

The choice of the level of industry disaggregation in the typology is guided by categories that facilitate meaningful economic analysis subject to data constraints. The statistical analysis is based on the latest available World Bank Enterprise Survey for Brazil, China, India, Russia, Egypt and Nigeria. This sample covers low- and middle-income countries from different regions of the world and provides representative firm-level information at the two-digit ISIC sector level. Seventeen sub-sectors/industries<sup>6</sup> are part of the stratification of each enterprise survey and therefore considered in the analysis to follow.

# <u>Methodology</u>

As an illustrative example, consider the distinction between small, medium and large firms. The numbers presented in Table 5 indicate the average share of large firms across different industries, where ‘large’ firms are defined as those with more than 100 employees. This ‘average’ represents a simple average for the 6 countries in the sample where the number for each country, in turn, is a simple average of the sampled firms. For each country in the sample, data from the most recent enterprise survey are used. The corresponding Z-scores for the numbers in Table
.003<sup>*</sup>|||0.000|
|||(-1.78)|||(0.08)|
|Household headship (ref: husband is head)||||||
|<br>Wife head of household||0.028|||0.067<sup>**</sup>|
|||(1.61)|||(2.46)|
|Other male household head||-0.035|||-0.084<sup>**</sup>|
|||(-122)|||(-266)|
|Other female household head||.<br>-0.072<sup>***</sup>|||.<br>-0.155<sup>***</sup>|
|||(-2.85)|||(-3.63)|
|Constant|2.195<sup>**</sup><br>0.499|0.137|3.566<sup>**</sup>|2.737|1.994|
||(2.38)<br>(0.39)|(0.12)|(2.07)|(1.63)|(1.25)|
|Observations<br>|71,313<br>71,313|71,122|142,585|142,585|142,324|
|_R_<sup>2</sup>|0.05<br>0.06|0.11|0.08|0.11|0.13|

_Note:_ Data for 41 countries from 2010-18. _t_ statistics in parentheses. Dependent variable is wife's housing
Policy Research Working Paper 8796

# **Abstract**

Electrification has been shown to accelerate opportunities for women by moving them into more productive activities, but whether improvements in economic outcomes also change gender norms and practices within the household remains unclear. This paper investigates the causal link between electricity access and women’s empowerment, using a large gender-disaggregated data set on India. Empowerment is measured by women’s decision-making ability, mobility, financial autonomy, reproductive freedom, and social participation. Using propensity score matching, the study finds that electrification enhances all measures of women’s empowerment and is associated with an 11-percentage point increase in the overall empowerment index. Employment and education are identified as the two most important causal channels through which electrification enables empowerment.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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/research. The authors may be contacted at fzhang1@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
included in Appendix E.

An “omniscient” adult household member was interviewed for each household, preferably the individual with the most information about the household budget, and multiple people could contribute if the primary respondent did not have responses for questions about other household members (for instance, regarding incomes or work arrangements). The design closely followed protocols applied to Living Standards Monitoring Surveys (LSMS) type surveys. The national sample was stratified by region and by rural/urban areas. These data were cross-validated with data on 8
close to the observed poverty levels. Moreover, our preferred specification allows for much indepth distributional analysis by incorporating the evolution of labor and non-labor income components. While a three-sector model also provides competitive estimates, it is evident that expanding the set of sectors allows for a more comprehensive distributional analysis. This is desirable despite the added noise, as our focus is on the accuracy of the estimates rather than the precision. For FY12, the prediction accuracy of models goes down, which is an expected outcome for predictions further into the past.<sup>16</sup>

**Table 5: Comparison between Observed and Backcasted Poverty**

|**Method**|**FY16**|**FY14**|**FY12**|
|---|---|---|---|
|Official Poverty|24.3|29.5|36.3|
|Preferred specification: equation (1)|28.8|31.4|30.7|
|PovertyGrowth Elasticity|24.0|25.5|26.8|
|Neutral distribution(0.86passthrough)|29.6|32.4|31.6|
|Democratic|29.6|34.8|39.6|
|Plutocratic|29.7|35.0|39.7|
|3-Sectors|28.7|31.2|29.8|
|11-Sectors|28.8|31.4|30.7|

Source: Authors’ estimations based on HIES 2018/19 and the Household Integrated Income and Consumption Survey (HIICS) 2015/16.

Note: Preferred specification follows equation (1), including information from nominal growth from eleven economic sectors to model labor income and household-specific inflation as described in the methodology. All other projections rely on headline inflation and nominal growth rates. Growth and poverty changes between FY16 and FY19 are used to estimate the elasticity for the
>9</sup> : i where SIDi is the Simpson Index of Diversification for farmer _i_ and is the proportionate cultivated area dedicated to the _k_ th crop. A high value indicates that a farmer cultivates many iikk PP crops, in an evenly distributed way, which is consistent with the notion of high diversification. A zero score implies that the farmer cultivates only one crop, which is consistent with complete specialization. The average SID for female farmers is estimated to be 0.060 which is

> 9 SID is equal to one minus the Herfindahl index, which is widely used to measure industry concentration.

10
<!-- Start of picture text --> a SPI = 0.56*** ODIN + 27.57 200<br>. * R*2—= 0.33 SPI = 0.71*** MENA MAI + 33.67<br>R2=0.52<br>_..<br>S a 8 ee<br>2& ~ . & we<br>.<br>5:5<br>2& : nq2 20 ..<br>o<br>° ° ° ° ° ° “ oO 20 40 60 80<br>2020 ODIN Overall Score for MENA Countries MEN|A Microdata Access Indicator<br><!-- End of picture text -->
bought time for banks and sovereigns, key underlying problems remain.** Market participants are concerned that reduced funding pressures due to ECB support may have diminished incentives to restructure balance sheets and to seek alternative, more sustainable ways to boost capital. Likewise, the LTROs can perpetuate unviable ―zombie‖ banks, which may continue to take risks. For governments, pressures to reform may have eased as borrowing costs decline. A flood of liquidity may also harbor the risk of bubbles in the bond, equity, and real estate markets. The encumbered LTRO collateral that banks put up has increased risk for senior bank bondholders, which might inhibit unsecured market funding going forward. Last, by loading up on sovereign bonds, banks have reversed a selling trend, realigning their fate

> 9 Most of the net €190 billion liquidity infusion of the first LTRO is believed to have been used by Spanish and Italian banks to purchase bonds with high yields. The larger LTRO in February (net ~€320 billion) is believed to have improved lending conditions, as more small banks participated and carry trade became slightly less attractive. Spanish banks bought €20.1 billion of government bonds in March (or €81 billion in the preceding four months), and Italian banks bought €24 billion of government debt (or €67 billion in the preceding four months), bringing Euro Area bank purchases to €142 billion in 2012:Q1. With most of the liquidity injection used by Italian and Spanish banks, the sovereign yield curves for Spain and Italy have decreased further, particularly on the short end. 10 Indeed, private credit conditions remained weak in February 2012, and only an estimated €10 billion of the December 2011 LTRO (5 percent) reportedly found its way to the real economy as credit.

> 11 The ECB data shows that Greek bank deposits fell from a peak of Є245 billion in end-2009 to Є170 billion in February 2012, while Spanish deposits fell by Є6.4 billion in February 2012, after a Є17
# Figure **III.4** Indonesia--Policy responses and incentives to borrow abroad

<!-- Start of picture text --> Monetary policy: components of reserve money Fiscal policy: fiscal impulse and demand<br>growth pressures<br>Indonesia<br>percent Indonesia percent<br>120 - 2 -<br>Q_ | ~ ~ ~ ~~~~~~~~~~~~~~~~~~-2<br>100 , -3<br>1080 1990 1991 1992 1993 1904 1905 1990 ¶997 t999 1990 1991 1992 1993 1094 1990 199b 1597<br>_contribution of domestic credit to reserve money growth E x aOOScess demand<br>a=contribution of net foreign assets to reserve mooey growth _ Fiscat imputs<br>reserve money growth (percent of GDP)<br><!-- End of picture text -->

60 -~~~~~~~~~~~~~~~~~~~--0 Fiscal impulse estimates based on the non oil sector

-40 ~~ ~ **~** ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~0 -60 -~~~~~~~~~~~~~~~~29 Differentials between domestic deposit rates and loo ~~~~~~~~~~~~~~~~~-3-9Nominal exchange rate and variability of U'S LIBOR unpredictable component of nominal exchange rate movements.

<!-- Start of picture text --> todoceeja<br>percent p~~~~~~~~~~~~~~~~~~fercentIne<br>s5 Indonesia so- -112<br>g _ ~~~~~~~~~~~~~~~- t109<br>20- -104<