rafmacalaba/data-use-mentions-extended huggingface dataset:
relative to that of 2008 (Figure 4).

In 2010, the average levels of GDP in advanced economies and in Eastern Europe and Central

> 9 We use Consensus Forecasts or World Economic Outlook estimates when actual 2010 GDP data are not available. 10 These economies are Albania, Azerbaijan, Equatorial Guinea, Djibouti, Greece, Guyana, Haiti, Kyrgyz Republic, Pakistan, The Gambia, Uganda, and Vanuatu.

12
represented almost 50 percent of the outstanding amount of nonfinancial corporate bonds in emerging economies at the end of 2019. Most likely, a substantial share of this maturing debt will have to be rolled over, but it is unclear whether investors will still be interested in buying emerging market nonfinancial corporate bonds when the time to refinance comes. Willingness to rollover the debt will depend, among other factors, on whether global interest rates are low enough to maintain investors’ appetite for riskier assets in emerging economies as well as investors’ assessment on the overall vulnerability of firms.<sup>5</sup> In addition to the declining bond maturities, the increasing reliance on bond financing might have exposed emerging economy firms to higher rollover risks per se because bonds are more difficult to restructure than bank loans. The idea is that the dispersion of bond investors reduces their incentives to renegotiate debt payments relative to banks, which can monitor firms more closely and offer greater flexibility for firms than bondholders in times of distress (Bolton and Scharfstein, 1996; Hackbarth et al., 2007; Crouzet, 2018). Another factor influencing the greater financial flexibility of bank loans with respect to bonds is that loan creditors are senior in the event of default (whereas bond holders usually are not), so lenders can recover the principal more easily (Schwert, 2019).

While some developments increased rollover risk, other forces might have helped to reduce it. Even if bond maturities declined, the compositional change from loan to bond financing could have a positive effect on aggregate (loan plus bond) debt maturities because bond debt is usually longer term than bank debt (Cortina et al., 2019). As a result, the overall effect on debt maturities is unclear. Analyzing whether overall debt maturities increased or decreased for emerging market firms since the GFC would require data on bank loan maturities, which are not typically available across economies.

> 5 The COVID-19 pandemic could have exacerbated problems with debt rollovers. Aggregate estimates for all emerging economies (excluding
The construction of these outcome variables is detailed in Table A.1 in the Appendix. Descriptive statistics are shown in Table A.2 in the Appendix.

# **4.4 Internal validity**

We discuss four threats to the identification of causal impacts: imbalances between the treatment and control groups, spillovers, attrition, and multiple testing.

**Balance** The treatment and control groups are well balanced across a series of baseline demographic and economic characteristics (Table A.2 in the Appendix). For all but one variable analyzed, the magnitude of the differences between the treatment and control groups is smaller than 0.1 standard deviations of the control group, which indicates good balance (Imbens and Rubin 2015, p. 310). Only two out of 75 differences are statistically significant at conventional thresholds. The p-values of omnibus F-tests of joint significance are above 0.90 for both treatment arms. Overall, these results suggest that the control and treatment groups are well-balanced.

**Spillovers** Important spillovers between the treatment and control groups are unlikely given the relatively small scale of the program—only 0.3% of the rural population of Jendouba received a cash grant.

**Attrition** Attrition could be an issue in this study, especially since the endline survey took place about 2-2.5 years after the intervention and 4-4.5 years after the baseline survey. Furthermore, previous research has shown that cash transfer interventions can affect migration decisions (Angelucci 2015; Adhikari and Gentilini 2018; Imbert and Papp 2019; Gazeaud et al. 2021a).

The overall attrition rate in our study is 8.8% (Table A.3), which is relatively low and on par with comparable studies in other developing country contexts. The attrition rate in the control group is 11.2%, which is significantly larger than the attrition rate of 6% and 6.8% observed in the two treatment groups. Differential attrition is mainly driven by higher migration rates
**9 Tables**

Table 1: Afrobarometer Round 4 (2008)

|Country|Number of Respondents|
|---|---|
|Benin|1200|
|Botswana|1200|
|Burkina Faso|1200|
|Cape Verde|1264|
|Ghana|1200|
|Kenya|1104|
|Lesotho|1200|
|Liberia|1200|
|Madagascar|1350|
|Malawi|1200|
|Mali|1232|
|Mozambique|1200|
|Namibia|1200|
|Nigeria|2324|
|Senegal|1200|
|South Africa|2400|
|Tanzania|1208|
|Uganda|2431|
|Zambia|1200|
|Total N|26513|

Table 2: Experiences with paying taxes and fees across Africa (Afrobarometer 2008)

||Income Tax|Property Tax|Public Utility Fee|Fee for Service|License Fee|
|---|---|---|---|---|---|
|Ghana|38.83|34.00|65.50|58.58|24.08|
|South Africa|38.63|41.21|63.71|59.29|14.88|
|Mali|33.36|24.68|23.70|63.23|39.77|
|Cape Verde|29.21|34.31|81.13|83.99|17.09|
|Namibia|26.08|28.50|62.92|80.92|23.67|
|Nigeria|25.60|23.62|55.46|60.71|26.98|
|Mozambique|24.44|37.90|44.20|72.56|46.83|
|Kenya|23.07|24.23|42.33|80.43|43.01|
|Botswana|17
land or area under specific crops at the national or other administrative level). For the analysis of household level processes and outcomes, on the other hand, it is vital that the land area being measured can then be linked to other variables concerning the agricultural production, or welfare outcomes, or other variables of interest for the same household or holding.<sup>3</sup> The main types of surveys for which these measurements are relevant are agricultural sample surveys, agricultural censuses, multi-topic surveys that cover agriculture (such as most Living Standard Measurement Study (LSMS) surveys), and smaller scale household surveys carried out for research purposes.

This paper aims to provide some elements to inform the selection of measurement methods, based on empirical evidence gathered by the Living Standard Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team of the World Bank in Ethiopia, Tanzania, and Nigeria during methodological fieldwork aimed at understanding the relationship between farmer estimates, GPS measurement and the traditional compass and rope method. This paper focuses on the analysis of area measurement data from the abovementioned studies. For more detailed guidance on implementation and practical considerations for area measurement in household surveys, refer to the complementary guidebook (Carletto et al., 2016).

# **II. Methods for land area measurement in surveys**

Achieving accurate area measures in a household survey setting with limited financial resources and a potentially strict fieldwork schedule is a challenging endeavor. Numerous considerations need to be balanced in designing the survey and selecting the appropriate method including the logistics of transportation to plots, the length of the full questionnaire and the likelihood of respondent and/or enumerator fatigue, and the security of teams and equipment. Three main methods are used in the context of agricultural statistics data collection for the measurement of land area: compass and rope, traditionally held as the ‘ gold standard ’ ; respondent self-reported land area; and, the most recent addition, GPS-based measurement. Each area measurement option has unique costs and benefits that need to be
requirements before entering the teaching profession. Regarding the former, to upgrade existing teachers’ skills and qualifications, teachers will need continuous support. This is needed to increase the efficiency of their instruction and to build their practical pedagogical and digital skills, which are critical domains for learning recovery and acceleration (World Bank et al. 2022b).

In the case of the EAP region, what is the _relative_ importance of these two challenges: the need to upgrade the skills of the existing _stock_ of teachers versus the importance of hiring more (and better preparing) the _flow_ of new teachers? Using a simulation described below, this paper tries to quantify the relative importance of the two.

# III. Simulation: Teacher stock and flow in primary education in East Asia and Pacific middle-income countries, 2021–2030

## A simple model to forecast the relative importance of the stock versus the flow of teachers

As mentioned above, there are already estimates of how many teachers will be needed in the future. These estimates are based on simulation models.<sup>5</sup> To our knowledge, in these modeling exercises, there has been less focus on the importance of the _existing_ stock of teachers. Our paper helps fill this gap in a simple and, we hope, intuitive way.

The model is based on several factors, such as: (i) the size of future cohorts of children (which are exogenous to the education sector); (ii) the proportion of those cohorts who will end up in school (for which we make assumptions about net enrollment rates); (iii) the pupil-to-teacher ratio (PTR, a policy variable); and (iv) teacher attrition (for which we use historical data where available).

We hope this paper will be a useful input for policy makers’ and development partners’ deliberations on how to improve teaching quality.

Box 1. Simulation model: Steps, assumptions, and robustness check

|**A. Seven steps **|
|-
male agrees that a female should tolerate violence, Men can beat women under certain circumstances is a binary indicator for the male disagreeing that “a man should not beat a woman under any circumstance,” and the Violence Attitudes index is generated by taking the unweighted mean of the indicators after they have been standardized to the mean and standard deviation among males whose connected female is in a ELA only community and is not assigned to the _Goal_ intervention at baseline and endline separately. Higher values of the index indicate greater taste for violence. IPV often (in last year) index is defined as for the females in Table 2, except the underlying questions are about the male’s own perpetration of psychological, physical, and sexual abuse. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Male baseline and endline data, balanced panel.

50
Policy Research Working Paper 9302

# **Abstract**

This paper examines the determinants of corporate savings in a cross-country panel setting. Specifically, it employs firm-level data covering more than 540,000 firm-year observations for 12 advanced and emerging market economies. Panel regression results suggest that reductions in statutory corporate income tax rates can explain one-third of the rise in corporate savings (defined as net financial assets) in 2003–17. This finding is supported by a propensity scores matching analysis of the effects of changes in corporate income tax rates.

This paper is a product of the Prospects Group. 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 atsushi.kawamoto@gmail.com.

_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
on selected datasets.

# _<u>Best Practices Database</u>_

Over 2,150 award-winning solutions to common social, economic and environmental problems in over 140 developing and developed countries. Searchable by country, scale (global, national, regional, village, etc.) and by subject category. Best Practices is a joint product of UNHABITAT and The Together Foundation and is supported in part by the Dubai Municipality, the Best Practices Partners, and the Government of UK. Coverage: 1996 - present.

# _<u>Creditor Reporting System (CRS)</u>_

Contains data on Official Development Assistance (ODA), Official Aid (OA) and other lending to developing countries and countries in transition. Data are collected from the Members of the Development Assistance Committee, the World Bank, and the regional financial institutions. The system is sponsored jointly by the OECD and the World Bank and operated by the OECD.

# _<u>DAC Online</u>_

These statistics, collected annually from the Members of the OECD's Development Assistance Committee, measure the flows of aid and other financial resources to aid recipients (developing 38 __________________________________________________________________________________________________________
Figure 2. Share of Venezuelan Migrants and VenRePS Sample <!-- Start of picture text --> Venezuelans (2018 Census)<br>Missing Information<br>1 - 8<br>9 - 42<br>43 - 240<br>241 - 166,566<br>Survey Sample<br>0<br>1 - 180<br>181 - 240<br>241 - 480<br>481 - 4,376<br><!-- End of picture text --> _Notes:_ The figure presents the share of Venezuelan migrants by 2018 and the sample of individuals surveyed in VenRePS 2020.

23
operations and reduce the time taken for clearance of shipments. It classifies imported items into different risk categories by tracking historical data on shipments that were previously refused admission into the US. Again, this process is suggestive of path dependence and reputation effects of the type that are the focus of this paper.

The FDA makes refusals information public in their Import Refusal Report (IRR), which is generated from OASIS data. Reports provide information on the manufacturer’s name and country of origin, as well as the dates and motives for the refusal. To gain access to historical refusals data, we submitted a Freedom of Information Act request in September 2009, which the FDA satisfied by supplying data in May 2010.

Product codes supplied by the exporter allow for a very specific definition of the transformation process the imported product has undergone (raw, dried, and pasteurized etc.) and it is usually precise enough to define a straightforward correspondence with the HS classification. The only part of the correspondence in which additional issues arise involves the FDA process code relating to “packaged food” under which exporters/importers tend to regroup various types of products that sometimes should have been coded otherwise. Thus, more careful handling was necessary. First, according to product subclasses providing information on containers, we make the straightforward assumption that products in metal and glass containers are transformed, and thus fall into HS Chapter 16 for “fish and fishery products”, and HS Chapter 20 for “fruit and fruit products”, and “vegetables and vegetable products”. Second, for containers of different materials, we analyzed product description data in which the FDA agent filled in a precise definition of the product. We also verified the information about refused products on companies’ websites.

# **3.2 Other Data**

In addition to the novel dataset on FDA import refusals discussed in the previous subsection, we use standard data sources for the remaining variables used in our analysis (Table 1). We source trade data 11
training is far more difficult in Brazil than in the US. The second issue is whether it is correct to make the same assumptions as CHS do about expenditures not observed directly. For example, new product development in finance is a major component of our intangible estimates, but what if this is an overestimate due to the crude assumption that 20 percent of costs in this industry go towards product development? Interestingly, a recent firm-level survey conducted in the UK (Marrano and Haskel 2010) attempts to measure this item directly, and the results suggest that the 20 percent assumption is optimistic even for the UK.

One important way forward is to start incorporating questions on different types of intangible expenditures in firm surveys already conducted by official agencies. A desirable end-goal would be to have all firms report their investment in specific categories of globally-recognized intangible assets as 34
In order to identify the impact of institutional structures on financial conditions and corporate investment in China, we present a simple illustrative model by adapting the theoretical constructs from Kaplan and Zingales (1997, 2000) and Fazzari, Hubbard, and Petersen (2000) to the Chinese context in which firms are subject to differential costs of external financing depending on their government connections. Given the highly regulated nature of the formal financial system in China, Chinese local and central governments have a fiscal incentive to channel credit to firms that pay taxes, remit profits, and support their economic, social and political initiatives (Gordon and Li, 2009, 2011). These firms include state-owned firms and nonstate firms with strong government connections. To the extent that firms with strong government connections face lower costs of external funds, their investment should be less responsive to internally generated cash flows than firms with weak government connections, for which the availability of internal funds will likely dictate investment patterns. Our model, therefore, points to inter-linkages between government connections, firms’ cash flow, and capital investment behavior.

Employing the World Bank’s survey of manufacturing firms in 120 Chinese cities conducted in 2005, our empirical results suggest that on average Chinese firms do face financial constraints in that their investment rates are sensitive to internal cash flows, as has been shown in China using other data sets. In addition we show that their investment is sensitive to access to bank loans, trade credit and the level of unpledged collateralizable assets (henceforth “UCA”), all indicators of access to external finance. That the cash flow variable is still significant when we include the external financing variables in the regressions suggests that internal financing is crucial for investment. In short, external finance is not enough to fund all investment 3
exercise for other parts of the world. The Sahel region and the Horn of Africa are important regions, that are different than the countries we have examined here, in terms of both agro-ecology and governance.

# **3.3 Heterogeneous Impacts across Household Types**

Given that budgets are limited, it would be very useful for interventions that aim to mitigate the impacts of droughts to be able to identify ex-ante (at a low-cost) children in households that are most likely to be affected. While this may seem straightforward, there are both conceptual as well as measurement challenges involved. From a conceptual perspective, should interventions target children that are in the poorest households, or children close to the stunting threshold, or children with profiles that have historically exhibited the largest drought impacts?<sup>26</sup> If children that are close to the stunting threshold are also more vulnerable to drought shocks, and in the poorest households, then supporting this group of children would address all three concerns. However, as we show below, these categories may not fully overlap.

From a measurement perspective, the set of surveys that capture child anthropometrics, as well as monetary poverty measures, both of acceptable quality, is very small. The DHS data on child anthropometrics are of reasonable quality, but the surveys do not have measures. any economic We are left with using the wealth indices, which are imperfect, for reasons discussed earlier.

Brown et al. (2017) show that around half of all undernourished children and underweight females, in their sample of 30 countries in Sub-Saharan Africa, are found in the top three DHS wealth quintiles. They use this fact to infer that a large fraction of poor individuals do not live in poor households, as defined by the wealth index. While this is certainly plausible, in our view, at least part of what they attribute to intra-household inequality or local health effects could merely reflect the error associated with measuring a household’s wealth status.<sup>27
” and where it was specifically noted that investment policies favor large-scale projects over medium- and small-scale projects in which women are more likely to become engaged. A 2016 study based on a crosssectional survey of 250 women from Sinnar State reaffirms this observation, showing that 79 percent of the women who did not possess any assets were classified as poor (Elsheikh and Elamin 2016, 198). The study concludes by suggesting that, “In order to enable women to develop their full economic potential 37
Table 10c: Impacts of PROSPERA on School Enrollment: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 Nearest-Neighbor Matching models

|||Enrollment<br>||
|---|---|---|---|
|Variables|7thgrade 2009-2010|9thgrade 2011-2012|12thgrade 2014-2015|
||Propensityscore matchingw|ith exact matchingon age a|nd state|
|Girls|0.097***|0.089***|0.039**|
||(0.0199)|(0.019)|(0.018)|
|Observations|4,93|4,931|4,931|
|Boys|0.085***|0.127***|0.081***|
||(0.018)|(0.018)|(0.017)|
|Observations|4,751|4,751|4,751|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Turning to type of school, Table 11a through 11c shows effects on type of secondary school enrolled.

Table 11a: Impacts of PROSPERA on Type of Secondary School 6<sup>th</sup> Graders Enrolled in