rafmacalaba/data-use-mentions-extended huggingface dataset:
disappear among the sample of owners in Appendix Table A5), the value of non-residential and residential land was not significantly different across men and women in these countries. In Tanzania, however, women’s value of non-residential and residential land is significantly lower than men’s.

Figure 1, which relies on the asset value specification (C) (multiple imputation with self-reported values for joint owners),<sup>13</sup> shows that important intra-household differences can be masked when only considering the distribution of household wealth. Even among the top 20<sup>th</sup> quintile of household wealth, for example, around 20-30 percent of men and women in these households have either no assets or are in the bottom two quintiles of the individual wealth distribution. Table 5, which presents results from OLS household fixed effects regressions, shows that women in this group are more likely to be young (30 years of age or less), with less schooling, and less likely to be married (for men in this group, on the other hand, only age had a significant positive association). Figure 1 also shows that in Tanzania, Malawi and Ethiopia, among households in the top two quintiles (above 60<sup>th</sup> percentile) of household wealth, the share of women in the top two quintiles of individual wealth is smaller than for men.

> 13 The results were similar for the other specifications and are available upon request.

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to ―run‖ just to keep their existing relative ranking in income per capita.

**Figure 8:  G7 and BRIC Shares of World GNI, 2000-2009**

<!-- Start of picture text --> 70%<br>60%<br>50% G7 Share (Market E-rates)<br>40%<br>G7 Share (PPP$)<br>30%<br>BRIC Share (PPP$)<br>20%<br>10% BRIC Share (Market E-Rates)<br>0%<br>2000 2009<br><!-- End of picture text --> Source: World Development Indicators.

In terms of their contribution to economic growth—the driving force behind these changing shares—we also see that large emerging market economies have now moved ―up the 13
<!-- Start of picture text --> DaR<br>2 and Corporate Insolvency<br>120<br>TZA<br>100 JOR<br>80<br>MNE LKA<br>URY<br>60 BIHBIH MAR BOLBOL GHA<br>IND LTU KEN URY TUR UKRUKR<br>40 CHNTHAMYS BGR IDN LKA ARGARGPAKPAK VNM NGA ZWE<br>TUN VNM<br>20 SVNKAZPOLCZELVA CHNSRBSRBTHA EST MYS HUN COLCOLMEXMEXIND GEOROMJAMCHL IDN ZAFZAFBRABRATUR PER ZMB PHLPHLTUN PRY JOR NGA BGDBGD ZWE ECU<br>0<br>0 10 20 30 40 50 60 70 80 90<br>Corporate Insolvency Index, Average Percentile Rank, high = vulnerable<br>DaR2: no shock DaR2: 30% earnings shock + 30% receivables shock<br>DaR2, percent<br><!-- End of picture text --> Note: this figure presents information for 56 countries for which we have data on both Debt at Risk and on corporate insolvency index.

**In light of the inefficiencies observed in the insolvency systems across EMDEs, some may turn to out-of-court workouts (OCWs) as a more efficient alternative to court-supervised insolvency proceedings for the resolution of corporate distress.** Specifically, governments may need to develop a framework for encouraging negotiations between private debtors and their creditors, which has been a successful tool in past crises. As informal tools, OCWs can be put in place relatively quickly and work as complements or alternatives to the formal insolvency system.<sup>9</sup>

# 7. References

Banerjee, Ryan, Anamaria Illes, Enisse Kharroubi and José-Maria Serena (2020). Covid-19 and corporate sector liquidity. Bank for International Settlements Bulletin No.10, 2020.

Feyen, Erik, Norbert Fiess, Igor Zuccardi, Igor, Lara Pillonca (2017). Which emerging markets and developing economies face corporate balance sheet vulnerabilities?: a novel monitoring framework (English). Policy Research working paper; no. WPS 8198.
comprehensive estimates of bilateral immigrant stocks worldwide in 2010 (World Bank 2010a).

# 5. Discussion of results

The model has performed well during the global financial crisis. Despite initial concerns of a sharp decline, the actual decline in remittances in 2009 has been similar to our forecasts. The model predicted correctly that remittance flows to developing countries would decline only modestly, unlike foreign direct investment and portfolio debt and equity flows. The initial forecasting exercise prepared in November 2008—see Ratha, Mohapatra and Xu (2008)—predicted that remittance flows to developing countries would fall by 1 percent in 2009 in the base case scenario and no more than 6 percent in a low-case scenario (where the last year’s flow of migrants was forced to return). Remittance flows were forecast to decline in five of the six developing regions (other than the East Asia and Pacific region) in the base case scenario. As the financial crisis deepened and the World Bank and the IMF revised down their growth forecasts, the model-generated forecasts of remittances were also revised downward, to 5-8 percent decline in in March 2009, and 7-10 percent decline in July 2009 (Ratha and Mohapatra 2009; Ratha, Mohapatra and Silwal 2009a).

The actual outcome in 2009 was a 6 percent decline in remittance flows to developing countries (Ratha, Mohapatra and Silwal 2009b, 2010). In terms of regional distribution, remittance flows declined in five of the six developing regions, but the extent of declines in some regions were larger and in others smaller than initially predicted—East Asia & Pacific, Middle East and North Africa, South Asia and SubSaharan Africa did better while Europe & Central Asia and Latin America & the Caribbean performed worse. However, as initially predicted, remittances remained more resilient compared to other types of private resource flows to developing countries.

There are several limitations of the forecasting methodology outlined above. First, the model does not explicitly feature return migration, a key risk factor. We do not have data on return migration for most
and decision-making patterns, which we do not have. The same applies to income data from EU-SILC. Therefore, allocating at the individual level requires significant assumptions. In our case, we assume adult equivalence scales. Given the strong assumption, we also conduct the analysis at the household level. We transition our focus away from exclusively examining child poverty to encompass a broader perspective that considers poverty within households where children reside. This shift allows us to explore and understand the dynamics of poverty within the context of families and households, taking into account not only the well-being of children but also the overall economic and social circumstances of the entire household unit. By adopting this broader perspective, we aim to gain a more comprehensive understanding of the complexities and interdependencies that shape poverty experiences in households with children.

**This paper contributes to the existing literature on fiscal incidence for children by proposing an approach where households are categorized based on their demographic characteristics; these household profiles allow us to assess the unique challenges and vulnerabilities faced by this demographic group before and after fiscal policy, and design targeted policies or interventions to address their specific needs.** We extend the standard CEQ methodology by constructing household typologies based on sociodemographic profiles. These household typologies and information on quintiles provide a helpful picture of the differential impact of tax/transfer systems on poverty among households with different sociodemographic profiles.  For example, we can identify that some groups of Bulgarian households with children remain vulnerable and do not necessarily receive sufficient support from fiscal policy. These are households that deserve more attention from a social policy standpoint. This fundamental policy question cannot be answered, lacking typologies. Finally, because the household categories are shared across a broad range of countries, this methodology can be easily replicated by analysts and policymakers interested in gaining a more accurate picture of tax incidence. This approach has been recently used in the recent gendered fiscal incidence literature (Robayo-Abril et al., 2023; Greenspun, 2019; Grown and Valodia, 2010).

**We created four household
Policy Research Working Paper 10252

# **Abstract**

Can mobile phone data improve program targeting? By combining rich survey data from the baseline of a “big push” anti-poverty program in Afghanistan implemented in 2016 with detailed mobile phone logs from program beneficiaries, this paper studies the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. The paper shows that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.

This paper is a product of the Development Impact Evaluation Group, Development Economics. 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 gbedoya@worldbank.org or acoville@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
Amounts||||-0.034|-0.004|-0.005|
|||||(0.086)|(0.075)|(0.074)|
|Lagged GPS*Large IDA Aid Amounts||||0.011<br>(0.022)|0.016<br>(0.019)|0.017<br>(0.019)|
|Region-Year FE|No|Yes|Yes|No|Yes|Yes|
|GPS without outliers|No|No|Yes|No|No|Yes|
|N|1954|1954|1954|1954|1954|1954|
|R-squared|0.023|0.158|0.158|0.002|0.159|0.159|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally. A recipient received large Chinese or IDA investments if total investments over the entire time period are above the 75th percentile. All regressions use recipient-year observations and include a constant term that is not reported. Year level clusterrobust standard errors reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent thy difference is that compared to other donors, particularly bilateral donors, the number of recipient countries – with 52 on average in a given year – is very low.

For our estimation, we generate a Chinese investment dummy variable that equals one for a recipient if total investments over the entire time period exceed the 75th percentile, which corresponds to investments of about 2.4 billion USD. We include all investments recorded by AidData, which include ODA- and OOF-like flows. Because that dataset covers the period from 2000 to 2014, we use that time span in the regressions. Columns (1) to (3) in Table 8 present
28 account to improve less rapidy than the capital account deteriorates, then there will be a decrease in the supply of foreign reserves available to the government, YFGi. If we refer to Diagram 1 we see that the government's demand for foreign assets, xFGi, also falls wich the devaluation. Thus, in this case, if the fall in the supply of foreign assets, YFGi, is more rapid than the reduction in the government's demand for assets, then the devaluation will have led to an increase in excess demand for foreign assets, and will thereby be destabilizing. This could, in particular, happen in a floating exchange rate regime where the government fixes its demand for reserves. In the numerical work reported in the next section, this instability has not proven to be a problem. In general, the closer to being perfect substitutes are domestic and foreign assets, the more likely will be non-convexity in equation (3.27), and hence unstable behavior of the model. A mechanical, and possibly economically unsatisfactorily way of coping with this situation would be to impose restriction on capital flows, which in this case would consist in imposing a ceiling on the net increase in holdings of foreign assets permitted to consumers.

# V. AN APPLICATION TO MEXICO

In this section we shall attempt to apply our model to the case of Mexico. We have estimated a number of the structural elements in the theoretical model using Mexican data, and then simulated the model over a three year period, representing 1983-85, in order to see whether it generates some approximation to Mexican reality. We then attempt to determine the implications of changes in certain government policies. It should be noted that the model is not fully esnimated, so the results we
(LSMS-ISA) data for Ethiopia (also known as the Ethiopia Socioeconomic Survey (ERSS)) in order to investigate the time-use patterns of household members as well as the impacts of shocks to agriculture on food consumption within the household (CSA, 2012, 2014, 2016). The LSMS-ISA is a household survey conducted in eight countries in Sub-Saharan Africa (SSA), focusing predominantly on rural households and their agricultural activities. There have been four waves/ survey rounds conducted for Ethiopia in 2011-12, 2013-14, 2015-16, and 2018-19. For our analysis, we specifically concentrate on the second and third waves of data. This selection is based on the sampling consistencies across these waves, ensuring the reliability of the collected data.

The LSMS-ISA surveys employ a two-stage random sampling design. First, enumeration areas (EAs) are selected at random, followed by a random selection of households

> 11To understand the distribution of hot days within these 10 km buffers, refer to Figure A4 in the Online Appendix. It provides a map of the variation in temperatures that is not explained by school, year, and stream fixed effects.

7
did (the child) do any kind of work for someone who is not a member of this household? (b) During the past week, did (the child) help with household chores such as shopping, collecting firewood, cleaning, fetching water, or caring for children? (c) During the past week did (the child) do any other family work (on the farm or in a business or selling goods in the street)? 19 When data on parents’ education was not available (parents were not identified clearly in all of the surveys), the highest education level of the adult females in the household was used for mother’s education, and the highest education level of adult males in the household was used for father’s education.

6
br><!-- End of picture text --> In the literature on credit cycles, the primary variable under observation is the real private credit growth. To compute credit growth, we start with a series of stocks of credit. The WDI data set of the World Bank contains series for the domestic credit to private sectors as a percentage of GDP. We calculate the real private credit growth using different ratios. In Table II-1 we report the three separate measures, that we calculated using this data to obtain the actual credit growth. Given the availability of data for different ratios, we find the private domestic credit (%GDP) * Real GDP to be the most convenient measure of real credit.

**Table II-1: Measures of Credit**

|Variable|Calculation|
|---|---|
|Real_Private_Credit_ USD|private domestic credit (% GPD) * Real GDP|
|Real_Private_Credit_ USD_CPI|private domestic credit (% GPD) * Nominal GDP / CPI|
|Real_Private_Credit_LCU_DEF|private domestic credit (% GPD) * GDP (LCU) / GDP deflator|

7
same 30-year period.

Our interpretation of these results is that capital controls and limited banking system depth have prevented capital from flowing to viable projects in less financially open and developed economies. Were this not the case, we would not observe a change in returns as economies open and develop.<sup>22</sup> Overall, these results are consistent with the idea that misallocation creates profit opportunities for private equity investors. As financial systems open and become more competitive, “home run” investments are harder to find. Table A11 tests whether the effect of financial openness can be explained by changes in perceived corruption, which may also have changed during this reform. The direction of the result in Column (6) of Table 8 is robust to this control.

It is worth relating these findings to what is known about flows of private equity investments across countries. Lerner et al. (2009) use data from Capital IQ to construct a database of 76,398 private equity investments made in 1984-2008 across 123 countries. Although they do not measure returns, they find associations between country characteristics and investment volume as a share of GDP. The authors report that private equity investment flows to countries with higher GDP per capita and less corruption, although not to markets with less private sector credit to GDP. Although their paper examines a different sample of investors with preferences that potentially differ from the IFC’s, one way to reconcile their results with ours is that the greatest volume of investment need not necessarily go to markets with the highest returns; indeed, returns could be highest in markets with lower private sector credit to GDP precisely because there is less investment in those markets.

> 21Table A8 reports results using the IRR as measure of performance. Table A9 reports results when restricting the sample to only realized investments. Results are qualitatively similar.

> 22Table A10 reports results without country fixed effects for comparison. The fact that associations become stronger when we include country fixed effects indicates that the largest differences in returns across economies occur
# **5. CONCLUDING OBSERVATIONS**

In sum, the simulations of this paper suggest that the expected expansion in mining offers Liberia an opportunity to embark on an era of rapid and inclusive growth, including progress in terms of both economic and social indicators. The extent to which the government will be able to avail itself of this opportunity will depend on government efficiency (allocative and productive) and policy choices, among other things influencing the extent to which less advantaged groups have access to government services and infrastructure.

Throughout the simulated period Liberia has a very large albeit diminishing GDP share for the government (including off-budget aid). This can continue as long as a large share of government receipts is from foreign aid and taxes on enclave sectors. However, if such receipts decline significantly relative to GDP, it may be very difficult and counterproductive to fully make up for these losses by raising additional local taxes. If so, spending would have to decline relative to GDP.

While expansion of infrastructure promises to offer stronger and quicker payoffs in economic growth, it is important to consider the fiscal consequences of operating and maintaining this larger infrastructure in the context of projections for government financial resources and consideration of other spending needs.

Finally, data on Liberia’s economy are subject to a high degree of uncertainty, both in terms of overall size and disaggregated data, making ongoing efforts to improve Liberia’s database very important. The development of the Supply and Use Tables (SUTs) were a prerequisite for the development of the database for the current analysis, including an inputoutput table and a SAM. Among many areas, improving data on public sector activities, both onand off-budget, seem particularly crucial for a well-informed analysis of Liberia’s economy.

14
therefore do national trade balances.

# **_Equilibrium and calibration_**

Given the transport network, endowments of labor and land, and parameters of the model (including � productivity parameters z�� and country origin‐destination shift parameters A�� ), the model generates, in each cell, prices for all goods, wages, and land rents such that markets clear. These prices shape the behavior of firms and the income levels and behavior of households, such that the associated levels of supply and demand are equal. The calibration sets values of productivity � parameters z�� and country origin‐destination shift parameters A�� such that equilibrium employment in each sector and cell matches the employment data, per capita incomes match country level income data, and international trade flows match the trade data.

# **_Simulations_**

The calibrated model is then ‘shocked’ with new transport times in the trade network, and firms and households adjust their decisions to optimally respond to the new geography of the region. We explore different degrees of response under combinations of five different assumptions which are, in summary:

- (1) Direct effect:  To quantify the scale of the shock we calculate transport cost savings at unchanged base level prices and quantities, i.e. take the transport cost reduction times the base value of exports from each cell to all other cells.<sup>4</sup> This allows us to observe the direct

> 3  If cells are grouped by country then the matrix of values of A��� is, for each s, a block matrix with common value A��� � A��� ,  for all i ε country c , j ε country k , and values unity on the block diagonal, i, j in the same country.

> 4  This is an ‘out of equilibrium’ calculation, designed purely to quantify the size of the initial shock.

8
# **Figure 17: Direct job content per $1 million of gross exports across macro sectors 2011**

<!-- Start of picture text --> Agriculture<br>Construction<br>Electricity, gas and water<br>Financial services<br>Manufacturing<br>Mining and Energy<br>Other commercial services<br>Public services<br>Real estate, renting<br>Trade, sales and accomodation<br>Transport and communication<br>0 100 200 300 400<br>Jobs per $1 mil. exports<br>ZAF BRA RUS CHL COL THA TUR<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

## **5. Labor Content of Exports in Global Value Chains**

In this section, we narrow our focus and measure the labor content of non-resources intensive manufacturing exports traditionally governed in GVCs, to examine how growth in non-traditional export sectors has impacted the level and nature of labor in South Africa. South Africa has become more integrated in GVCs, and is looking to upgrade in GVCs as an avenue to generating higher quality jobs (Farole 2015). GVCs are fundamentally about restructuring the location of economy activity: what used to be produced under one factory roof may now take place across a number of different countries. This has fundamental impacts on where jobs go, who gets them, and what types of jobs they are (Farole 2015). A priori, the net impact of joining and upgrading in GVCs on labor markets in developing countries is unclear and not well studied in the literature,<sup>16</sup> as it can occur in opposite directions through a variety of channels.<sup>17</sup> Separating the domestic content from the foreign content contained in a country’s exports—as achieved in the LACEX data—is a necessary step when disentangling the impact of production across multiple borders. However